Feature Selection Using Principal Feature Analysis

Total Page:16

File Type:pdf, Size:1020Kb

Feature Selection Using Principal Feature Analysis ACM Multimedia, Augsburg, Germany, September 23-29, 2007. Feature Selection Using Principal Feature Analysis Yijuan Lu Ira Cohen Xiang Sean Zhou Qi Tian University of Texas at San Hewlett-Packard Labs Siemens Medical Solutions University of Texas at San Antonio 1501 Page Mill Road USA, Inc. Antonio One UTSA Circle Palo Alto, CA 94304 51 Valley Stream Parkway One UTSA Circle San Antonio, TX, 78249 [email protected] Malvern, PA, 19355 San Antonio, TX, 78249 [email protected] xiang.zhou@siemens. [email protected] com ABSTRACT Dimensionality reduction of a feature set is a common 1. INTRODUCTION In many real world problems, reducing dimension is an essential preprocessing step used for pattern recognition and classification step before any analysis of the data can be performed. The general applications. Principal Component Analysis (PCA) is one of the criterion for reducing the dimension is the desire to preserve most popular methods used, and can be shown to be optimal using different optimality criteria. However, it has the disadvantage that of the relevant information of the original data according to some optimality criteria. In pattern recognition and general measurements from all the original features are used in the classification problems, methods such as Principal Component projection to the lower dimensional space. This paper proposes a Analysis (PCA), Independent Component Analysis (ICA) and novel method for dimensionality reduction of a feature set by Fisher Linear Discriminate Analysis (LDA) have been extensively choosing a subset of the original features that contains most of the used. These methods find a mapping from the original feature essential information, using the same criteria as PCA. We call this method Principal Feature Analysis (PFA). The proposed method space to a lower dimensional feature space. is successfully applied for choosing the principal features in face In some applications it might be desired to pick a subset of the tracking and content-based image retrieval (CBIR) problems. original features rather then find a mapping that uses all of the Automated annotation of digital pictures has been a highly original features. The benefits of finding this subset of features challenging problem for computer scientists since the invention of could be in saving cost of computing unnecessary features, saving computers. The capability of annotating pictures by computers cost of sensors (in physical measurement systems), and in can lead to breakthroughs in a wide range of applications excluding noisy features while keeping their information using including Web image search, online picture-sharing communities, “clean” features (for example tracking points on a face using easy and scientific experiments. In our work, by advancing statistical to track points- and inferring the other points based on those few modeling and optimization techniques, we can train computers measurements). about hundreds of semantic concepts using example pictures from Variable selection procedures have been used in different settings. each concept. The ALIPR (Automatic Linguistic Indexing of Among them, the regression area has been investigated Pictures - Real Time) system of fully automatic and high speed extensively. In [1], a multi layer perceptron is used for variable annotation for online pictures has been constructed. Thousands of selection. In [2], stepwise discriminant analysis for variable pictures from an Internet photo-sharing site, unrelated to the selection is used as inputs to a neural network that performs source of those pictures used in the training process, have been pattern recognition of circuitry faults. Other regression techniques tested. The experimental results show that a single computer for variable selection are described in [3]. In contrast to the processor can suggest annotation terms in real-time and with good regression methods, which lack unified optimality criteria, the accuracy. optimality properties of PCA have attracted research on PCA based variable selection methods [4, 5, 6, 7]. As will be shown, Categories and Subject Descriptors these methods have the disadvantage of either being too I.4.7 [Feature Measurement] computationally expensive, or choosing a subset of features with redundant information. This work proposes a computationally General Terms efficient method that exploits the structure of the principal Algorithms, Theory, Performance, Experimentation components of a feature set to find a subset of the original feature vector. The chosen subset of features is shown empirically to Keywords maintain some of the optimal properties of PCA. Feature Extraction, Feature Selection, Principal Component Analysis, Discriminant Analysis The rest of the paper is as follows. The existing PCA based feature selection methods are reviewed in Section 2. The Permission to make digital or hard copies of all or part of this work for proposed method, Principal Feature Analysis (PFA), is described personal or classroom use is granted without fee provided that copies are in Section 3. We apply PFA to face tracking and content-based not made or distributed for profit or commercial advantage and that copies image retrieval problems in Section 4. Discussion will be given in bear this notice and the full citation on the first page. To copy otherwise, Section 5. or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MM’07, September 23-28, 2007, Augsburg, Bavaria, Germany. Copyright 2007 ACM 978-1-59593-701-8/07/0009...$5.00. 2. BACKGROUND AND NOTATION i’th coefficient of one of the principal components (PC) implies We consider a linear transformation of a random vector that the xi element of X is very dominant in that axes/PC. By n choosing the variables corresponding to the highest coefficients of X ∈ℜ with zero mean and covariance matrix Σ x to a lower each of the first q PC’s, the same projection as that computed by q PCA is approximated. This method is a very intuitive and dimension random vector Y ∈ ℜ , q < n computationally feasible method. However, because it considers T each PC independently, variables with similar information content Y = Aq X (1) might be chosen. T with Aq Aq = Iq where I q is the q × q identity matrix. The method proposed in [6] and [7] chooses a subset of size q by computing its PC projection to a smaller dimensional space, and In PCA, A is a n× q matrix whose columns are the q q minimizing a measure using the Procrustes analysis [8]. This orthonormal eigenvectors corresponding to the first q largest method helps reduce the redundancy of information, but is eigenvalues of the covariance matrix Σ x . There are ten optimal computationally expensive because many combinations of subsets properties for this choice of the linear transformation [4]. One are explored. important property is the maximization of the “spread” of the In our method, we exploit the information that can be inferred by points in the lower dimensional space which means that the points the PC coefficients to obtain the optimal subset of features. But in the transformed space are kept as far apart as possible, and unlike the method in [5], we use all of the PC’s together to gain a therefore retaining the variation in the original space. Another better insight on the structure of our original features. So we can important property is the minimization of the mean square error choose variables without redundancy of information. between the predicted data and the original data. Now, suppose we want to choose a subset of the original 3. PRINCIPAL FEATURE ANALYSIS variables/features of the random vector X. This can be viewed as a Let X be a zero mean n-dimensional random feature vector. Let Σ linear transformation of X using a transformation matrix be the covariance matrix of X. Let A be a matrix whose columns ⎡ Iq ⎤ are the orthonormal eigenvectors of the matrix Σ: A (2) T q = ⎢ ⎥ Σ = AΛA (6) ⎣[0](n−q)×q ⎦ or any matrix that is permutation of the rows of A . Without loss ⎡λ1 ⎤ q ⎢ ⎥ of generality, lets consider the transformation matrix Aq as given . 0 where Λ = ⎢ ⎥ , λ ≥ λ ≥ ... ≥ λ above and rewrite the corresponding covariance matrix of X as ⎢ 0 . ⎥ 1 2 n ⎡ {Σ11}q×q {Σ12}q×(n−q) ⎤ ⎢ ⎥ Σ = ⎢ ⎥ (3) ⎣ λn ⎦ ⎣{Σ21}(n−q)×q {Σ22}(n−q)×(n−q) ⎦ λ , λ ,...λ are the eigenvalues of Σ and AT A = I . Let In [4] it is shown that it is not possible to satisfy all of the 1 2 n n q optimality properties of PCA using the same subset. Finding the Aq be the first q columns of A and let V1 ,V2 ,...Vn ∈ℜ be the subset which maximizes is equivalent to | ΣY |=| Σ11 | rows of the matrix Aq. maximization of the “spread” of the points in the lower Each vector Vi represents the projection of the i’th feature dimensional space, thus retaining the variation of the original (variable) of the vector X to the lower dimensional space, that is, data. the q elements of Vi correspond to the weights of the i’th feature Minimizing the mean square prediction error is equivalent to on each axis of the subspace. The key observation is that features minimizing the trace of that are highly correlated or have high mutual information will Σ = Σ − Σ Σ −1Σ . (4) have similar absolute value weight vectors Vi (changing the sign 22|1 22 21 11 12 has no statistical significance [5]). On the two extreme sides, two This can be seen since the retained variability of a subset can be independent variables have maximally separated weight vectors; measured using while two fully correlated variables have identical weight vectors ⎛ ⎞ (up to a change of sign). To find the best subset, we use the ⎜ ⎟ structure of the rows Vi to first find the subsets of features that are trace(Σ 22|1 ) n 2 Retained Variability = ⎜1− ⎟⋅100% ∑ σ i (5) highly correlated and follow to choose one feature from each ⎜ n 2 ⎟ i=1 subset.
Recommended publications
  • 10 Oriented Principal Component Analysis for Feature Extraction
    10 Oriented Principal Component Analysis for Feature Extraction Abstract- Principal Components Analysis (PCA) is a popular unsupervised learning technique that is often used in pattern recognition for feature extraction. Some variants have been proposed for supervising PCA to include class information, which are usually based on heuristics. A more principled approach is presented in this paper. Suppose that we have a pattern recogniser composed of a linear network for feature extraction and a classifier. Instead of designing each module separately, we perform global training where both learning systems cooperate in order to find those components (Oriented PCs) useful for classification. Experimental results, using artificial and real data, show the potential of the proposed learning system for classification. Since the linear neural network finds the optimal directions to better discriminate classes, the global-trained pattern recogniser needs less Oriented PCs (OPCs) than PCs so the classifier’s complexity (e.g. capacity) is lower. In this way, the global system benefits from a better generalisation performance. Index Terms- Oriented Principal Components Analysis, Principal Components Neural Networks, Co-operative Learning, Learning to Learn Algorithms, Feature Extraction, Online gradient descent, Pattern Recognition, Compression. Abbreviations- PCA- Principal Components Analysis, OPC- Oriented Principal Components, PCNN- Principal Components Neural Networks. 1. Introduction In classification, observations belong to different classes. Based on some prior knowledge about the problem and on the training set, a pattern recogniser is constructed for assigning future observations to one of the existing classes. The typical method of building this system consists in dividing it into two main blocks that are usually trained separately: the feature extractor and the classifier.
    [Show full text]
  • Feature Selection/Extraction
    Feature Selection/Extraction Dimensionality Reduction Feature Selection/Extraction • Solution to a number of problems in Pattern Recognition can be achieved by choosing a better feature space. • Problems and Solutions: – Curse of Dimensionality: • #examples needed to train classifier function grows exponentially with #dimensions. • Overfitting and Generalization performance – What features best characterize class? • What words best characterize a document class • Subregions characterize protein function? – What features critical for performance? – Subregions characterize protein function? – Inefficiency • Reduced complexity and run-time – Can’t Visualize • Allows ‘intuiting’ the nature of the problem solution. Curse of Dimensionality Same Number of examples Fill more of the available space When the dimensionality is low Selection vs. Extraction • Two general approaches for dimensionality reduction – Feature extraction: Transforming the existing features into a lower dimensional space – Feature selection: Selecting a subset of the existing features without a transformation • Feature extraction – PCA – LDA (Fisher’s) – Nonlinear PCA (kernel, other varieties – 1st layer of many networks Feature selection ( Feature Subset Selection ) Although FS is a special case of feature extraction, in practice quite different – FSS searches for a subset that minimizes some cost function (e.g. test error) – FSS has a unique set of methodologies Feature Subset Selection Definition Given a feature set x={xi | i=1…N} find a subset xM ={xi1, xi2, …, xiM}, with M<N, that optimizes an objective function J(Y), e.g. P(correct classification) Why Feature Selection? • Why not use the more general feature extraction methods? Feature Selection is necessary in a number of situations • Features may be expensive to obtain • Want to extract meaningful rules from your classifier • When you transform or project, measurement units (length, weight, etc.) are lost • Features may not be numeric (e.g.
    [Show full text]
  • Feature Extraction (PCA & LDA)
    Feature Extraction (PCA & LDA) CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline What is feature extraction? Feature extraction algorithms Linear Methods Unsupervised: Principal Component Analysis (PCA) Also known as Karhonen-Loeve (KL) transform Supervised: Linear Discriminant Analysis (LDA) Also known as Fisher’s Discriminant Analysis (FDA) 2 Dimensionality Reduction: Feature Selection vs. Feature Extraction Feature selection Select a subset of a given feature set Feature extraction (e.g., PCA, LDA) A linear or non-linear transform on the original feature space ⋮ ⋮ → ⋮ → ⋮ = ⋮ Feature Selection Feature ( <) Extraction 3 Feature Extraction Mapping of the original data onto a lower-dimensional space Criterion for feature extraction can be different based on problem settings Unsupervised task: minimize the information loss (reconstruction error) Supervised task: maximize the class discrimination on the projected space In the previous lecture, we talked about feature selection: Feature selection can be considered as a special form of feature extraction (only a subset of the original features are used). Example: 0100 X N 4 XX' 0010 X' N 2 Second and thirth features are selected 4 Feature Extraction Unsupervised feature extraction: () () A mapping : ℝ →ℝ ⋯ Or = ⋮⋱⋮ Feature Extraction only the transformed data () () () () ⋯ ′ ⋯′ = ⋮⋱⋮ () () ′ ⋯′ Supervised feature extraction: () () A mapping : ℝ →ℝ ⋯ Or = ⋮⋱⋮ Feature Extraction only the transformed
    [Show full text]
  • Feature Extraction for Image Selection Using Machine Learning
    Master of Science Thesis in Electrical Engineering Department of Electrical Engineering, Linköping University, 2017 Feature extraction for image selection using machine learning Matilda Lorentzon Master of Science Thesis in Electrical Engineering Feature extraction for image selection using machine learning Matilda Lorentzon LiTH-ISY-EX--17/5097--SE Supervisor: Marcus Wallenberg ISY, Linköping University Tina Erlandsson Saab Aeronautics Examiner: Lasse Alfredsson ISY, Linköping University Computer Vision Laboratory Department of Electrical Engineering Linköping University SE-581 83 Linköping, Sweden Copyright © 2017 Matilda Lorentzon Abstract During flights with manned or unmanned aircraft, continuous recording can result in a very high number of images to analyze and evaluate. To simplify image analysis and to minimize data link usage, appropriate images should be suggested for transfer and further analysis. This thesis investigates features used for selection of images worthy of further analysis using machine learning. The selection is done based on the criteria of having good quality, salient content and being unique compared to the other selected images. The investigation is approached by implementing two binary classifications, one regard- ing content and one regarding quality. The classifications are made using support vector machines. For each of the classifications three feature extraction methods are performed and the results are compared against each other. The feature extraction methods used are histograms of oriented gradients, features from the discrete cosine transform domain and features extracted from a pre-trained convolutional neural network. The images classified as both good and salient are then clustered based on similarity measures retrieved using color coherence vectors. One image from each cluster is retrieved and those are the result- ing images from the image selection.
    [Show full text]
  • Time Series Feature Extraction for Industrial Big Data (Iiot) Applications
    Time Series Feature Extraction for Industrial Big Data (IIoT) Applications [1] Term paper for the subject „Current Topics of Data Engineering” In the course of studies MSc. Data Engineering at the Jacobs University Bremen Markus Sun Born on 07.11.1994 in Achim Student/ Registration number: 30003082 1. Reviewer: Prof. Dr. Adalbert F.X. Wilhelm 2. Reviewer: Prof. Dr. Stefan Kettemann Time Series Feature Extraction for Industrial Big Data (IIoT) Applications __________________________________________________________________________ Table of Content Abstract ...................................................................................................................... 4 1 Introduction ...................................................................................................... 4 2 Main Topic - Terminology ................................................................................. 5 2.1 Sensor Data .............................................................................................. 5 2.2 Feature Selection and Feature Extraction...................................................... 5 2.2.1 Variance Threshold.............................................................................................................. 7 2.2.2 Correlation Threshold .......................................................................................................... 7 2.2.3 Principal Component Analysis (PCA) .................................................................................. 7 2.2.4 Linear Discriminant Analysis
    [Show full text]
  • Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards
    agronomy Article Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards Hong-Kun Lyu 1,*, Sanghun Yun 1 and Byeongdae Choi 1,2 1 Division of Electronics and Information System, ICT Research Institute, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea; [email protected] (S.Y.); [email protected] (B.C.) 2 Department of Interdisciplinary Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea * Correspondence: [email protected]; Tel.: +82-53-785-3550 Received: 20 November 2020; Accepted: 4 December 2020; Published: 8 December 2020 Abstract: Deep learning and machine learning (ML) technologies have been implemented in various applications, and various agriculture technologies are being developed based on image-based object recognition technology. We propose an orchard environment free space recognition technology suitable for developing small-scale agricultural unmanned ground vehicle (UGV) autonomous mobile equipment using a low-cost lightweight processor. We designed an algorithm to minimize the amount of input data to be processed by the ML algorithm through low-resolution grayscale images and image binarization. In addition, we propose an ML feature extraction method based on binary pixel quantification that can be applied to an ML classifier to detect free space for autonomous movement of UGVs from binary images. Here, the ML feature is extracted by detecting the local-lowest points in segments of a binarized image and by defining 33 variables, including local-lowest points, to detect the bottom of a tree trunk. We trained six ML models to select a suitable ML model for trunk bottom detection among various ML models, and we analyzed and compared the performance of the trained models.
    [Show full text]
  • Towards Reproducible Meta-Feature Extraction
    Journal of Machine Learning Research 21 (2020) 1-5 Submitted 5/19; Revised 12/19; Published 1/20 MFE: Towards reproducible meta-feature extraction Edesio Alcobaça [email protected] Felipe Siqueira [email protected] Adriano Rivolli [email protected] Luís P. F. Garcia [email protected] Jefferson T. Oliva [email protected] André C. P. L. F. de Carvalho [email protected] Institute of Mathematical and Computer Sciences University of São Paulo Av. Trabalhador São-carlense, 400, São Carlos, São Paulo 13560-970, Brazil Editor: Alexandre Gramfort Abstract Automated recommendation of machine learning algorithms is receiving a large deal of attention, not only because they can recommend the most suitable algorithms for a new task, but also because they can support efficient hyper-parameter tuning, leading to better machine learning solutions. The automated recommendation can be implemented using meta-learning, learning from previous learning experiences, to create a meta-model able to associate a data set to the predictive performance of machine learning algorithms. Al- though a large number of publications report the use of meta-learning, reproduction and comparison of meta-learning experiments is a difficult task. The literature lacks extensive and comprehensive public tools that enable the reproducible investigation of the differ- ent meta-learning approaches. An alternative to deal with this difficulty is to develop a meta-feature extractor package with the main characterization measures, following uniform guidelines that facilitate the use and inclusion of new meta-features. In this paper, we pro- pose two Meta-Feature Extractor (MFE) packages, written in both Python and R, to fill this lack.
    [Show full text]
  • Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data Robert T
    Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data Robert T. Olszewski February 2001 CMU-CS-01-108 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Thesis Committee: Roy Maxion, co-chair Dan Siewiorek, co-chair Christos Faloutsos David Banks, DOT Copyright c 2001 Robert T. Olszewski This research was sponsored by the Defense Advanced Project Research Agency (DARPA) and the Air Force Research Laboratory (AFRL) under grant #F30602-96-1-0349, the National Science Foundation (NSF) under grant #IRI-9224544, and the Air Force Laboratory Graduate Fellowship Program sponsored by the Air Force Office of Scientific Research (AFOSR) and conducted by the Southeastern Center for Electrical Engineering Education (SCEEE). The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, AFRL, NSF, AFOSR, SCEEE, or the U.S. government. Keywords: Structural pattern recognition, classification, feature extraction, time series, Fourier transformation, wavelets, semiconductor fabrication, electrocardiography. Abstract Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pat- tern) and then classifies the object based on that description (i.e., the recognition). Two general approaches for implementing pattern recognition systems, statistical and structural, employ differ- ent techniques for description and classification. Statistical approaches to pattern recognition use decision-theoretic concepts to discriminate among objects belonging to different groups based upon their quantitative features.
    [Show full text]
  • Feature Extraction Using Dimensionality Reduction Techniques: Capturing the Human Perspective
    Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2015 Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective Ashley B. Coleman Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Computer Engineering Commons, and the Computer Sciences Commons Repository Citation Coleman, Ashley B., "Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective" (2015). Browse all Theses and Dissertations. 1630. https://corescholar.libraries.wright.edu/etd_all/1630 This Thesis is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. FEATURE EXTRACTION USING DIMENSIONALITY REDUCITON TECHNIQUES: CAPTURING THE HUMAN PERSPECTIVE A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science By Ashley Coleman B.S., Wright State University, 2010 2015 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL December 11, 2015 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION BY Ashley Coleman ENTI TLED Feature Extraction using Dimensionality Reduction Techniques: Capturing the Human Perspective BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science. Pascal Hitzler, Ph.D. Thesis Advisor Mateen Rizki, Ph.D. Chair, Department of Computer Science Committee on Final Examination John Gallagher, Ph.D. Thesis Advisor Mateen Rizki, Ph.D. Chair, Department of Computer Science Robert E. W. Fyffe, Ph.D. Vice President for Research and Dean of the Graduate School Abstract Coleman, Ashley.
    [Show full text]
  • Unsupervised Feature Extraction for Reinforcement Learning
    Faculteit Wetenschappen en Bio-ingenieurswetenschappen Vakgroep Computerwetenschappen Unsupervised Feature Extraction for Reinforcement Learning Proefschrift ingediend met het oog op het behalen van de graad van Master of Science in de Ingenieurswetenschappen: Computerwetenschappen Yoni Pervolarakis Promotor: Prof. Dr. Peter Vrancx Prof. Dr. Ann Now´e Juni 2016 Faculty of Science and Bio-Engineering Sciences Department of Computer Science Unsupervised Feature Extraction for Reinforcement Learning Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in de Ingenieurswetenschappen: Computerwetenschappen Yoni Pervolarakis Promotor: Prof. Dr. Peter Vrancx Prof. Dr. Ann Now´e June 2016 Abstract When using high dimensional features chances are that most of the features are not important to a specific problem. To eliminate those features and potentially finding better features different possibilities exist. For example, feature extraction that will transform the original input features to a new smaller dimensional feature set or even a feature selection method where only features are taken that are more important than other features. This can be done in a supervised or unsupervised manner. In this thesis, we will investigate if we can use autoencoders as a means of unsupervised feature extraction method on data that is not necessary interpretable. These new features will then be tested in a Reinforcement Learning environment. This data will be represented as RAM states and are blackbox since we cannot understand them. The autoencoders will receive a high dimensional feature set and will transform it into a lower dimension, these new features will be given to an agent who will make use of those features and tries to learn from them.
    [Show full text]
  • Alignment-Based Topic Extraction Using Word Embedding
    Alignment-Based Topic Extraction Using Word Embedding [0000 0003 3108 8407] [0000 0002 8408 3944] Tyler Newman − − − and Paul Anderson − − − College of Charleston, Charleston SC 29424, USA [email protected] [email protected] Abstract. Being able to extract targeted topics from text can be a useful tool for understanding the large amount of textual data that exists in various domains. Many methods have surfaced for building frameworks that can successfully extract this topic data. However, it is often the case that a large number of training samples must be labeled properly, which requires both time and domain knowledge. This paper introduces new alignment-based methods for predicting topics within textual data that minimizes the dependence upon a large, properly-labeled training set. Leveraging Word2Vec word embeddings trained using unlabeled data in a semi-supervised approach, we are able to reduce the amount of labeled data necessary during the text annotation process. This allows for higher prediction levels to be attained in a more time-efficient manner with a smaller sample size. Our method is evaluated on both a publicly available Twitter sentiment classification dataset and on a real estate text classification dataset with 30 topics. Keywords: Topic extraction · Text annotation · Text classification · Word vectors · Text tagging 1 Introduction Finding specific topics within textual data is an important task for many do- mains. A multitude of approaches for achieving this task have appeared in recent years [14], no doubt due to the ever growing amount of textual data available to organizations and researchers. In the most straightforward case, topic labels are known a priori and non-domain experts can be trained to manually label examples using systems such as Mechanical Turk [4].
    [Show full text]
  • Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification
    International Journal of Environmental Research and Public Health Article Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification Kwang Ho Park 1 , Erdenebileg Batbaatar 1 , Yongjun Piao 2, Nipon Theera-Umpon 3,4,* and Keun Ho Ryu 4,5,6,* 1 Database and Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea; [email protected] (K.H.P.); [email protected] (E.B.) 2 School of Medicine, Nankai University, Tianjin 300071, China; [email protected] 3 Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand 4 Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand 5 Data Science Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam 6 Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea * Correspondence: [email protected] (N.T.-U.); [email protected] or [email protected] (K.H.R.) Abstract: Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoi- etic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, Citation: Park, K.H.; Batbaatar, E.; we propose not only to build a classification model for five major subtypes using two kinds of losses, Piao, Y.; Theera-Umpon, N.; Ryu, K.H.
    [Show full text]