DOCSLIB.ORG

Kernel and Moment Based 0.1In Prediction and Planning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Kernel and Moment Based 0.1In Prediction and Planning Kernel and Moment based Prediction and Planning Applications to Robotics and Natural Language Processing CMU-RI-TR-18-13 February 19, 2018 Zita Marinho Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Geoffrey J. Gordon, Chair Siddhartha S. Srinivasa, CMU/University of Washington André F. T. Martins, Unbabel/IT, Instituto Superior Técnico João P. Costeira, Instituto Superior Técnico Shay B. Cohen, University of Edinburgh Mathew T. Mason, Carnegie Mellon University Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. This research was supported by the Portuguese Foundation for Science and Technology under grant SFRH/BD/52015/2012. © Zita Marinho, 2018 2 zita marinho Keywords— Sequence prediction, Method of Moments, Semi-supervised learning, Predictive State Representations, Reinforcement-Learning 1 Abstract This thesis focuses on moment and kernel-based methods for applications in Robotics and Natural Language Processing. Kernel and moment-based learning leverage information about correlated data that allow the design of compact representations and efficient learning algorithms. We explore kernel algorithms for planning by leveraging inherently continuous properties of reproducing kernel Hilbert spaces. We introduce a kernel based robot motion planner based on gradient optimization, in a space of smooth trajectories, a reproducing kernel Hilbert space. We further study a kernel-based approach in the context of prediction, for learning a generative model, and in the context of planning for learning to interact with a controlled process. Our work on moment-based learning can be decomposed into two main branches: spectral techniques and anchor-based methods. Spectral learning describes a more expressive model, which implicitly uses hidden state variables. We use it as a means to obtain a more expressive predictive model that we can use to learn to control an interactive agent, in the context of reinforcement learning. We propose a combination of predictive representations with deep reinforcement learning to produce a recurrent network that is able to learn continuous policies under partial observability. We introduce an efficient end-to-end learning algorithm that is able to maximize cumu- lative reward while minimizing prediction error. We apply this approach to several continuous observation and action environments. Anchor learning, on the other hand, provides an explicit form of representing state variables, by relating states to unambiguous observations. We rely on anchor-based techniques to provide a form of explicitly recovering the model parameters, in particular when states have a discrete representation such as in many Natural Language Processing tasks. This family of methods provides an easier form of integrating su- pervised information during the learning process. We apply anchor-based algorithms on word labelling tasks in Natural Language Processing, namely semi-supervised part-of-speech tagging where annotations are learned from a large amount of raw text and a small annotated corpus. Acknowledgements I would like to thank the many people that encouraged me and contributed during the period of my PhD. First of all I would like to thank all of my three academic guiding stars: André Martins, Geoff Gordon and Siddhartha Srinivasa for their incredible support and patience teaching me throughout the PhD. Thanks to their support and guidance I was able to learn from a rich and diverse set of research subjects. André was a very attentive and incredible advisor and I am very grateful to him for always directing me towards the right direction. He was my NLP guiding star. I would like to thank Geoff, my Machine Learning guid- ing star, for always being present throughout these years and for his insightful advice on many research and practical problems. And lastly, I would like to thank Sidd, my Robotics guiding star, for being truly inspiring and for providing all the support i needed in the PhD, thanks to him I was able to be a part of a inspiring and thriving lab, the Personal Robotics Lab, I would like to thank Mike Koval and Anca Dra- gan for collaborating and working with me during the PhD. I was able to learn a lot from their advice and experience. From the Geoff’s side of the family, I am very grateful to Byron Boots for his guidance in the RKHS project and for teaching me about PSRs, and to Ahmed Hefny for collaborating with me on the RPSP pa- per, his contribution was invaluable to the completion of this work. I am also very thankful to all my other collaborators: Wen Sun, Arun Byravan, and in particular to Gilwoo Lee for her work on learning PWS hybrid systems. Thank you also to Shay Cohen and Matt Mason for being part of my committee for their feedback and discussions on the thesis topics. I would like to thank my academic grandfather Noah Smith for welcoming me in his research group and taking the time to teach my independent study at CMU. Thank you to Prof. João Paulo Costeira for his enormous support and for welcoming me in the SIPG lab, and also all my colleagues at ISR in Lisbon: Shanghang, Qiwei, João Carvalho, João Saúde, Jayakorn, Beatriz, Sérgio, Cláudia and Manuel for the great environment, which I already miss terribly, and also Sabina and Susana for their immense support and friendship. I would like to say a special thank you to Ada Zhang, Renato Negrinho, João Saúde and Alessandro Giordano, for being great friends and house- mates. Thank you also to Priberam for hosting me in my first years of my PhD. This thesis could not have been possible without the support of the Fundação para a Ciência e Tecnolo- gia (FCT) and the Information and Communication Technology Institute (ICTI) through the CMU/Portugal program (SFRH/BD/ 52015/2012). This work has also been partially supported by the European Union under H2020 project SUMMA, grant 688139, and by FCT, through contracts UID/EEA/50008/2013, through the LearnBig project (PTDC/EEISII/7092/2014), and the GoLocal project (grant CMUPERI/TIC/0046/2014). Personally I would like to thank all my friends and family for their incredible support through the best of times and through the worst of times. Andreia and Sofia you have always had a sympathetic ear. Thank you Gil for your support, and André Moita, Cristina and Leonardo for all your advice, and Wang Ling for your great encouragement. 4 I would like to dedicate this thesis to my family, for which I am deeply grateful. And lastly, thank you Ana for being my sister, without your help none of this would have been possible. Contents 1 Introduction 8 1.1 Main contributions 14 1.2 Previous Publications 18 1.3 Thesis organization 18 2 Background 21 2.1 Notation 21 2.2 Models for sequential systems 22 2.3 Latent Variable Models 25 2.4 Latent Variable learning 26 2.5 Spectral learning of sequential systems 37 2.6 Planning approaches 54 2.7 Planning under uncertainty 57 3 Sequence Labeling with Method of Moments 61 3.1 Motivation 61 3.2 Sequence Labeling 63 3.3 Semi-Supervised Learning via Moments 65 3.4 Feature-Based Emissions 70 3.5 Method Improvements 73 3.6 Experiments 74 3.7 Conclusions 78 6 4 Planning with Kernel Methods 80 4.1 Motivation 80 4.2 Trajectories in Reproducing Kernel Hilbert Spaces 82 4.3 Motion Planning in an RKHS 84 4.4 Trajectory Efficiency as Norm Encoding in RKHS 87 4.5 Kernel Metric in RKHS 89 4.6 Cost Functional Analysis 91 4.7 Experimental Results 92 4.8 Conclusions 99 5 Planning with Method of Moments 100 5.1 Motivation 100 5.2 Predictive State Representations 103 5.3 Predictive Reinforcement Learning 104 5.4 Predictive State Representations of Controlled Models 106 5.5 Recurrent Predictive State Policy (RPSP) Networks 108 5.6 Learning Recurrent Predictive State Policies 109 5.7 Connection to RNNs with LSTMs/GRUs 114 5.8 Experiments 115 5.9 Results 116 5.10 Conclusions 121 6 Conclusions 123 6.1 Summary of contributions 123 6.2 Future directions 125 CONTENTS 7 1 Introduction In this chapter, we discus the core aspects of moment and kernel-based learning and summarize the main contributions made in this thesis. Method of Moments Many problems in machine learning attempt to find a compact model that is capable of explaining complex behaviours from observable quan- tities. We focus mostly on sequential observations, where time depen- dence plays a crucial role in the evolution of these events. Latent variable learning provides a compact representation for learn- ing these complex, high dimensional data in a structured form. How- ever, learning latent variables is often difficult, in part because of the non-convexity of the likelihood function (Sun, 2014; Terwijn, 2002). Yet, the main challenge is associated with estimating the latent (un- observed) states, which need to be estimated indirectly by looking at correlations among observations (examples in Figure 1.2. The method of moments (MoM) provides an alternative form for explaining high dimensional and complex data, based on classical statistics and probability theory. MoM dates back to Pearson’s solu- tion for curve fitting problems, i.e., for finding parameters that fit a mixture of two Gaussian distributions (Pearson, 1894). MoM’s esti- mation relies on the idea that empirical moments are “natural” esti- mators of population moments. In essence, learning a model via the MoM comes down to estimating model parameters that represent dis- tributions whose moments are in agreement with sample moments observed in the data. matching moments vs. maximum Moment-based algorithms differ from likelihood-based methods in likelihood that they attempt to recover parameters based on moment matching instead of maximizing likelihood which leads to intractable optimiza- tion.
Load more

Recommended publications
  • Engineering Applications of Artificial Intelligence Potential, Challenges and Future Directions for Deep Learning in Prognostics
    Engineering Applications of Artificial Intelligence 92 (2020) 103678 Contents lists available at ScienceDirect Engineering Applications of Artificial Intelligence journal homepage: www.elsevier.com/locate/engappai Potential, challenges and future directions for deep learning in prognostics and health management applications Olga Fink a,<, Qin Wang a, Markus Svensén b, Pierre Dersin c, Wan-Jui Lee d, Melanie Ducoffe e a Intelligent Maintenance Systems, ETH Zurich, Switzerland b GE Aviation Digital Group, United Kingdom c Alstom, France d Dutch Railways, Netherlands e Airbus, France ARTICLEINFO ABSTRACT Keywords: Deep learning applications have been thriving over the last decade in many different domains, including Deep learning computer vision and natural language understanding. The drivers for the vibrant development of deep learning Prognostics and health management have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. GAN Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition Domain adaptation monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing Fleet PHM Deep reinforcement learning and predicting faults of complex industrial assets has been limited. The current paper provides a thorough Physics-induced machine learning evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications. 1. Today's challenges in PHM applications by measurement and transmission noise. In many cases, the sensors are partly redundant, having several sensors measuring the same system The goal of Prognostics and Health Management (PHM) is to pro- parameter.
    [Show full text]
  • Implicit Kernel Learning
    Implicit Kernel Learning Chun-Liang Li Wei-Cheng Chang Youssef Mroueh Yiming Yang Barnabás Póczos {chunlial, wchang2, yiming, bapoczos}@cs.cmu.edu [email protected] Carnegie Mellon University and IBM Research Abstract discrepancy (MMD) (Gretton et al., 2012)isapowerful two-sample test, which is based on a statistics computed via kernel functions. Even though there is a surge of Kernels are powerful and versatile tools in deep learning in the past years, several successes have machine learning and statistics. Although the been shown by kernel methods and deep feature extrac- notion of universal kernels and characteristic tion. Wilson et al. (2016)demonstratestate-of-the-art kernels has been studied, kernel selection still performance by incorporating deep learning, kernel greatly influences the empirical performance. and Gaussian process. Li et al. (2015); Dziugaite et al. While learning the kernel in a data driven way (2015)useMMDtotraindeepgenerativemodelsfor has been investigated, in this paper we explore complex datasets. learning the spectral distribution of kernel via implicit generative models parametrized by In practice, however, kernel selection is always an im- deep neural networks. We called our method portant step. Instead of choosing by a heuristic, several Implicit Kernel Learning (IKL). The proposed works have studied kernel learning.Multiplekernel framework is simple to train and inference is learning (MKL) (Bach et al., 2004; Lanckriet et al., performed via sampling random Fourier fea- 2004; Bach, 2009; Gönen and Alpaydın, 2011; Duve- tures. We investigate two applications of the naud et al., 2013)isoneofthepioneeringframeworks proposed IKL as examples, including genera- to combine predefined kernels. One recent kernel learn- tive adversarial networks with MMD (MMD ing development is to learn kernels via learning spectral GAN) and standard supervised learning.
    [Show full text]
  • Exploring Induced Pedagogical Strategies Through a Markov Decision Process Frame- Work: Lessons Learned
    Exploring Induced Pedagogical Strategies Through a Markov Decision Process Frame- work: Lessons Learned Shitian Shen Behrooz Mostafavi North Carolina State University North Carolina State University [email protected] [email protected] Tiffany Barnes Min Chi North Carolina State University North Carolina State University [email protected] [email protected] An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a sys- tem that adaptively reacts to students’ behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address this challenge, we explore three aspects of a Markov Decision Process (MDP) framework through four experiments. The three aspects are: 1) reward function, detecting the impact of immediate and delayed reward on effectiveness of the policies; 2) state representation, explor- ing ECR-based, correlation-based, and ensemble feature selection approaches for representing the MDP state space; and 3) policy execution, investigating the effectiveness of stochastic and deterministic policy executions on learning. The most important result of this work is that there exists an aptitude-treatment interaction (ATI) effect in our experiments: the policies have significantly different impacts on the par- ticular types of students as opposed to the entire population. We refer the students who are sensitive to the policies as the Responsive group. All our following results are based on the Responsive group. First, we find that an immediate reward can facilitate a more effective induced policy than a delayed reward. Second, The MDP policies induced based on low correlation-based and ensemble feature selection ap- proaches are more effective than a Random yet reasonable policy.
    [Show full text]
  • Multiple-Kernel Learning for Genomic Data Mining and Prediction
    bioRxiv preprint doi: https://doi.org/10.1101/415950; this version posted September 13, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Multiple-kernel learning for genomic data mining and prediction Christopher M. Wilson1, Kaiqiao Li2, Pei-Fen Kuan2, and Xuefeng Wang1,2,* 1 Department of Biostatistics and Bioinformatics, Moffitt Cancer Center 2 Department of Applied Mathematics and Statistics, Stony Brook University * [email protected] Abstract Advances in medical technology have allowed for customized prognosis, diagnosis, and personalized treatment regimens that utilize multiple heterogeneous data sources. Multi- ple kernel learning (MKL) is well suited for integration of multiple high throughput data sources, however, there are currently no implementations of MKL in R. In this paper, we give some background material for support vector machine (SVM) and introduce an R package, RMKL, which provides R and C++ code to implement several MKL algorithms for classification and regression problems. The provided implementations of MKL are compared using benchmark data and TCGA ovarian cancer. We demonstrate that combining multiple data sources can lead to a better classification scheme than simply using a single data source. 1 Introduction 1 Integrating multiple heterogeneous high throughput data sources is an emerging topic 2 of interest in cancer research. Making decisions based upon metabolomic, genomic, 3 etc. data sources can lead to better prognosis or diagnosis than simply using clinical 4 data alone.
    [Show full text]
  • Clustering on Optimized Features
    [VOLUME 6 I ISSUE 1 I JAN. – MARCH 2019] e ISSN 2348 –1269, Print ISSN 2349-5138 http://ijrar.com/ Cosmos Impact Factor 4.236 PARAMETER-FREE INCREMENTAL CO- CLUSTERING ON OPTIMIZED FEATURES S. R. Sandhan1 & Prof. S. S. Banait2 1M.E. Student, 2Assistant Professor Department of Computer Engineering, K. K. Wagh Institute of Engineering Education & Research, Nashik, Savitribai Phule Pune University, Maharashtra, India Received: December 05, 2018 Accepted: January 16, 2019 ABSTRACT: To improve effectiveness and efficiency for clustering dynamic multi-modal data in cyber- physical-social systems, several co-clustering approaches have been proposed with the rapid growth of Cyber- Physical-Social Systems. Large amounts of dynamic multi-modal data are being generated and collected, so clustering Multi modal dynamic data is challenging. In high- dimensional dynamic data, classic metrics fail in identifying real similarities between objects. Moreover, the huge number of features makes the cluster interpretation hard. In the proposed method, the single-modality similarity measure is extended to multiple modalities and three operations, those are cluster creating, cluster merging, and instance partitioning, are defined to incrementally integrate new arriving objects to current clustering patterns without introducing additive parameters. Moreover, an adaptive weight scheme is designed to measure the importance of feature modalities based on the intra-cluster scatters and also add feature selection method for reducing time. Extensive experiments on various multimodal dynamic datasets demonstrate the effectiveness of the proposed approach. Key Words: Parameter-Free learning; Multi modal data; Clustering; Feature selection. I. Introduction Clustering is a popular data mining technique that enables to partition data into groups (clusters) in such a way that objects inside a group are similar to each other, and objects belonging to different groups are dissimilar.
    [Show full text]
  • Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation
    Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation Wei Wang,1 Hao Wang,1,2 Chen Zhang,1 Yang Gao1 1. Science and Technology on Integrated Information System Laboratory 2. State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences, Beijing 100190, China [email protected] Abstract port vector machine, logistic regression and statistic clas- sifiers, to the target domain. However, they do not ex- As a fundamental constituent of machine learning, domain plicitly explore the distribution inconsistency between the adaptation generalizes a learning model from a source do- main to a different (but related) target domain. In this paper, two domains, which essentially limits their capability of we focus on semi-supervised domain adaptation and explic- knowledge transfer. To this end, many semi-supervised do- itly extend the applied range of unlabeled target samples into main adaptation methods are proposed within the classifier the combination of distribution alignment and adaptive clas- adaptation framework (Chen, Weinberger, and Blitzer 2011; sifier learning. Specifically, our extension formulates the fol- Duan, Tsang, and Xu 2012; Sun et al. 2011; Wang, Huang, lowing aspects in a single optimization: 1) learning a cross- and Schneider 2014). Compared with supervised ones, they domain predictive model by developing the Fredholm integral take into account unlabeled target samples to cope with the based kernel prediction framework; 2) reducing the distribu- inconsistency of data distributions, showing improved clas- tion difference between two domains; 3) exploring multiple sification and generalization capability. Nevertheless, most kernels to induce an optimal learning space.
    [Show full text]
  • Study of Kernel Machines Towards Brain-Computer Interfaces Tian Xilan
    Study of kernel machines towards Brain-computer Interfaces Tian Xilan To cite this version: Tian Xilan. Study of kernel machines towards Brain-computer Interfaces. Artificial Intelligence [cs.AI]. INSA de Rouen, 2012. English. tel-00699659 HAL Id: tel-00699659 https://tel.archives-ouvertes.fr/tel-00699659 Submitted on 21 May 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Th`ese Pr´esent´ee devant l’Institut National des Sciences Appliqu´ees de Rouen pour obtenir Docteur en Sciences Mention Informatique Apprentissage et Noyau pour les Interfaces Cerveau-machine par Xilan TIAN Equipe: LITIS Ecole doctorale: SPMII soutenue le 7 Mai 2012 devant la commission d’examen Rapporteurs : H´el`ene Paugam-Moisy - Universit´ede Lyon Val´erie Louis-Dorr - INPL Directeur : St´ephane Canu - INSA de Rouen Encadrant : Gilles Gasso - INSA de Rouen Examinateurs : Marie-F Lucas - Ecole Centrale de Nantes Alain Rakotomamonjy - Universit´ede Rouen R´esum´e Les Interface Cerveau-Machine (ICM) ont ´et´eappliqu´ees avec succ`es aussi bien dans le do- maine clinique que pour l’am´elioration de la vie quotidienne de patients avec des handicaps. En tant que composante essentielle, le module de traitement du signal d´etermine nettement la performance d’un syst`eme ICM.
    [Show full text]
  • Multiclass Multiple Kernel Learning
    Multiclass Multiple Kernel Learning Alexander Zien [email protected] Cheng Soon Ong [email protected] Max Planck Inst. for Biol. Cybernetics and Friedrich Miescher Lab., Spemannstr. 39, T¨ubingen, Germany. Abstract evant and meaningful features [2, 13, 20]. In many applications it is desirable to learn Since in many real world applications more than two from several kernels. “Multiple kernel learn- classes are to be distinguished, there has been a lot of ing” (MKL) allows the practitioner to opti- work on decomposing multiclass problems into several mize over linear combinations of kernels. By standard binary classification problems, or developing enforcing sparse coefficients, it also general- genuine multiclass classifiers. The latter has so far izes feature selection to kernel selection. We been restricted to single kernels. In this paper we de- propose MKL for joint feature maps. This velop and investigate a multiclass extension of MKL. provides a convenient and principled way for We provide: MKL with multiclass problems. In addition, we can exploit the joint feature map to learn • An intuitive formulation of the multiclass MKL kernels on output spaces. We show the equiv- task, with a new sparsity-promoting regularizer. alence of several different primal formulations • A proof of equivalence with (the multiclass exten- including different regularizers. We present sion of) a previous MKL formulation. several optimization methods, and compare • Several optimization approaches, with a compu- a convex quadratically constrained quadratic tational comparison between three of them. program (QCQP) and two semi-infinite linear • Experimental results on several datasets demon- programs (SILPs) on toy data, showing that strating the method’s utility.
    [Show full text]
  • Bridging Deep and Kernel Methods
    Bridging deep and kernel methods Llu´ıs A. Belanche1 and Marta R. Costa-juss`a2 ∗ 1- Computer Science School - Dept of Computer Science Universitat Polit`ecnica de Catalunya Jordi Girona, 1-3, 08034 Barcelona, Catalonia, Spain 2- TALP Research Center - Dept of Signal Theory and Communications Universitat Polit`ecnica de Catalunya Jordi Girona, 1-3, 08034 Barcelona, Catalonia, Spain Abstract. There has been some exciting major progress in recent years in data analysis methods, including a variety of deep learning architec- tures, as well as further advances in kernel-based learning methods, which have demonstrated predictive superiority. In this paper we provide a brief motivated survey of recent proposals to explicitly or implicitly combine kernel methods with the notion of deep learning networks. 1 Introduction Multilayer artificial neural networks have experienced a rebirth in the data anal- ysis field, displaying impressive empirical results, extending even to classical ar- tificial intelligence domains, such as game playing, computer vision, multimodal recognition, natural language processing and speech processing. The versatility of such methods to perform non-linear feature extraction and deal with a di- versity of complex data –such as video, audio and text– has lead many-layered (“deep”) parametric models to get over well-established learning methods, like kernel machines or classical statistical techniques. Deep neural networks, for ex- ample, are very successful in machine learning applications although their num- ber of parameters is typically orders of magnitude larger than the number of training examples. However, their training is a delicate and costly optimization problem that raises many practical challenges. Learning several representational levels to optimize a final objective is the main trademark of deep learning, from which multiple tasks and fields have benefited lately.
    [Show full text]
  • A Multiple Kernel Density Clustering Algorithm for Incomplete Datasets in Bioinformatics Longlong Liao1,2, Kenli Li3*, Keqin Li4, Canqun Yang1,2 and Qi Tian5
    Liao et al. BMC Systems Biology 2018, 12(Suppl 6):111 https://doi.org/10.1186/s12918-018-0630-6 RESEARCH Open Access A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics Longlong Liao1,2, Kenli Li3*, Keqin Li4, Canqun Yang1,2 and Qi Tian5 From IEEE International Conference on Bioinformatics and Biomedicine 2017 Kansas City, MO, USA. 13-16 November 2017 Abstract Background: While there are a large number of bioinformatics datasets for clustering, many of them are incomplete, i.e., missing attribute values in some data samples needed by clustering algorithms. A variety of clustering algorithms have been proposed in the past years, but they usually are limited to cluster on the complete dataset. Besides, conventional clustering algorithms cannot obtain a trade-off between accuracy and efficiency of the clustering process since many essential parameters are determined by the human user’s experience. Results: The paper proposes a Multiple Kernel Density Clustering algorithm for Incomplete datasets called MKDCI. The MKDCI algorithm consists of recovering missing attribute values of input data samples, learning an optimally combined kernel for clustering the input dataset, reducing dimensionality with the optimal kernel based on multiple basis kernels, detecting cluster centroids with the Isolation Forests method, assigning clusters with arbitrary shape and visualizing the results. Conclusions: Extensive experiments on several well-known clustering datasets in bioinformatics field demonstrate the effectiveness of the proposed MKDCI algorithm. Compared with existing density clustering algorithms and parameter-free clustering algorithms, the proposed MKDCI algorithm tends to automatically produce clusters of better quality on the incomplete dataset in bioinformatics.
    [Show full text]
  • LMKL-Net: a Fast Localized Multiple Kernel Learning Solver Via Deep Neural Networks
    MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com LMKL-Net: A Fast Localized Multiple Kernel Learning Solver via Deep Neural Networks Zhang, Z. TR2018-072 July 12, 2018 Abstract In this paper we propose solving localized multiple kernel learning (LMKL) using LMKL-Net, a feedforward deep neural network. In contrast to previous works, as a learning principle we propose parameterizing both the gating function for learning kernel combination weights and the multiclass classifier in LMKL using an attentional network (AN) and a multilayer perceptron (MLP), respectively. In this way we can learn the (nonlinear) decision function in LMKL (approximately) by sequential applications of AN and MLP. Empirically on benchmark datasets we demonstrate that overall LMKL-Net can not only outperform the state-of-theart MKL solvers in terms of accuracy, but also be trained about two orders of magnitude faster with much smaller memory footprint for large-scale learning. arXiv This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2018 201 Broadway, Cambridge, Massachusetts 02139 LMKL-Net: A Fast Localized Multiple Kernel Learning Solver via Deep Neural Networks Ziming Zhang Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA 02139-1955 [email protected] Abstract In this paper we propose solving localized multiple kernel learning (LMKL) using LMKL-Net, a feedforward deep neural network.
    [Show full text]
  • Large Scale Multiple Kernel Learning
    Journal of Machine Learning Research 7 (2006) 1531–1565 Submitted 10/05; Published 7/06 Large Scale Multiple Kernel Learning Sören Sonnenburg [email protected] Fraunhofer FIRST.IDA Kekuléstrasse 7 12489 Berlin, Germany Gunnar Rätsch [email protected] Friedrich Miescher Laboratory of the Max Planck Society Spemannstrasse 39 Tübingen, Germany Christin Schäfer [email protected] Fraunhofer FIRST.IDA Kekuléstrasse 7 12489 Berlin, Germany Bernhard Schölkopf BERNHARD [email protected] Max Planck Institute for Biological Cybernetics Spemannstrasse 38 72076, Tübingen, Germany Editors: Kristin P. Bennett and Emilio Parrado-Hernández Abstract While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recy- cling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental re- sults show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for SVMs, especially when used with sparse feature maps as appear for string kernels, allowing us to train a string kernel SVM on a 10 million real-world splice data set from computational biology.
    [Show full text]