Analysis of Spherical Particle Distributions Observed on the WIND Spacecraft

Total Page:16

File Type:pdf, Size:1020Kb

Analysis of Spherical Particle Distributions Observed on the WIND Spacecraft 2007:087 MASTER'S THESIS Analysis of Spherical Particle Distributions Observed on the WIND Spacecraft Katharina Nowak Luleå University of Technology Master Thesis, Continuation Courses Space Science and Technology Department of Space Science, Kiruna 2007:087 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--07/087--SE Analysis of spherical particle distributions observed on the WIND spacecraft Katharina Nowak Wurzburg,¨ September 2007 Institut f¨ur Theoretische Physik und Astrophysik, Universit¨at W¨urzburg, Germany and Institutet f¨or rymdfysik, Lule˚atechniska universitet, Sweden Analysis of spherical particle distributions observed on the WIND spacecraft Katharina Nowak Wurzburg,¨ September 2007 Examiner: Prof. Dr. Wolfgang Droge¨ Institut fur¨ Theoretische Physik und Astrophysik, Universitat¨ Wurzburg,¨ Germany Second examiner (Sweden): Dr. Johnny Ejemalm Institutet for¨ rymdfysik, Lule˚a techniska universitet, Sweden Abstract WIND is a NASA spacecraft built to observe the solar wind. Since many phenomena like auroras are related to the solar wind and it has also an influence on all the spectra measured on Earth, therefore it is important to explore its behaviour. On board the spacecraft the SST, EESA and PESA instruments measure the electron and ion flux of the solar wind. These data have been further investigated. Access to existing data sets from the database of the WIND mission provided by University of Berkeley, USA, has been gained through some IDL library functions that have been specifically programmed for the purpose of preprocessing and presenting data. A statistical analysis of the present data is conducted using mostly Mathematica. After examining the data for false mea- surements and general characteristic attributes, like minima, maxima, mean value and variance, more sophisticated statistical derivations have been computed for gaining the knowledge about the spherical statistical behaviour which is thought to be describable by the von Mises-Fisher distribution or similar functional distributions. Therefore the data were examined first for the mean direction which defines one major part of the simulating, theoretical distribution expec- tation. It was found out that in the main solar events can be modelled by distributions defined on a sphere and therefore missing or wrong data can be reconstructed. i Contents 1 Introduction 1 1.1 Basicprinciples................................. .... 1 1.1.1 Thesunandthesolarwind . 1 1.1.2 Pitchangle ................................... 3 1.1.3 Thetransportequation . 4 1.2 WINDspacecraft.................................. 8 1.2.1 Instruments................................... 9 1.2.2 Datainformation............................... 11 1.3 Stereomission................................... 18 2 Analysis and Methods 21 2.1 Model .......................................... 21 2.1.1 von Mises-Fisher distribution . ...... 21 2.1.2 Kentdistribution.............................. 23 2.2 Simulationofsphericaldata . ....... 25 2.2.1 Calculating the mean, the major and the minor axis of the data separately 27 2.2.2 NMinimize ................................... 27 2.2.3 FindFit ..................................... 28 2.2.4 FindFitwithstartvalues . 28 2.2.5 Comparison of the results of the different methods . ......... 29 2.2.6 Reconstructionofdata. 35 2.3 Physicalmeaning................................. 37 3 Outcome 45 3.1 Issuesthatoccurred .............................. 45 3.2 Result .......................................... 45 3.3 Furtherwork ..................................... 46 Listoffigures....................................... 47 Listoftables ....................................... 51 References......................................... 52 ii CONTENTS CONTENTS A Appendix 58 A.1 Notations ....................................... 58 A.2 Mathematicanotebooks . 59 iii Chapter 1 Introduction 1.1 Basic principles 1.1.1 The sun and the solar wind The sun in the system of the Earth is the most interesting star in the whole universe even if it is an average star of the spectral class two. Due to the fact that is is just one astronomical unit (AU) away from the Earth, it is possible ”to study not only its electromagnetic radiation but also solar emissions of a different kinds - plasmas and energetic particles” ([1]). This is important to understand on the one hand other suns in the universe and on the other hand the effects of the Sun on the Earth, which can be seen in the atmosphere, in the weather on the Earth as well as for example in the field of biology. The chemical composition of this star is not different to other stars. About 92 % of the volume of the matter inside is hydrogen needed for the energy generation in the sun and nearly 8 % is helium, consisting of a leftover from the energy production due to nuclear fusion as well as originally present matter. In terms of mass these are 75 % hydrogen, 23 % helium and about 2 % are heavy elements, which is in total more than 99.8 % of the mass of the whole solar system ([2]). The temperature on the surface of the sun is approximately 5500 K. Further basic properties are summarised in table 1.1. The sun consists of different zones. The nuclear burning zone, which is also known as the Table 1.1: Properties of the sun [1], [3] Radius 696000 km Mass 1.99 1030 kg · Mean distance from the Earth 1 AU=150 106 km · Average density 1.91 g/cm3 Effective blackbody temperature 5785 K Radiation emitted (luminosity) 3.86 1026 W · Mass loss rate 109 kg/s core of the sun, is the innermost part, where about 50 % of its mass is concentrated. Since the energy is transported by radiation the second one is named radiation zone. From about 0.74 1 1.1. Basic principles 1.1.1 solar radii to the surface lies the hydrogen convection zone while the visible part itself is known as the photosphere. The solar atmosphere consists of three different parts and is located above the photosphere. These parts are the chromosphere, the transition region and the corona. The outer boundary of the corona is not well-defined but more hackly structured. The atmosphere itself is not stable. It emits a particle flow called solar wind. This was first demonstrated by E. N. Parker in 1958 ([4]). One year after Parker’s predictions it was measured by Lunik II, one of the first Soviet satellites. The US Venus probe Mariner 2 analysed the particles afterwards more precise and studied the number density, the different velocities of the particles and the magnetic field strength between the Earth and Venus, which is about 5 10 9 T · − in average. This weak magnetic field is directed almost parallel to the ecliptic plane. The 2 B 11 3 related energy density in the solar wind yields to Umag = 2µ0 = 10− Jm− . The direction of the interplanetary magnetic field lines frozen-in in the solar wind is not radial due to the rotation of the sun. The sidereal rotation period of the sun is 27 days. Directly at the outer boundary of the sun the solar wind flows radially away from its origin. But then the plasma circles westward and forms an Archimedian spiral. The deformation of the field lines is known as the garden-hose effect ([1]). This shape of the field lines and therefore also of the solar wind is not as steady-going in reality as it was described. There are several reasons, which would cause this deviation. One factor might be the different velocities of the streams of the solar wind. But also a change in the polarity of the interplanetary magnetic field seen from a position, which is not rotating with the sun, caused by the sector structure of the magnetic field of the sun. Another reason could be that coronal mass ejections (CMEs) or co-rotating interaction regions (CIRs) produce shocks, which generate discontinuities themselves. There is also a forth influence that should be named. Alfv´en waves originated by the sun could be a reason for the discontinuities, plasma waves and magneto-hydrodynamic waves with low frequencies, which are causing fluctuations. Those have an influence on the magnetic field described above due to superposition ([5]). The solar wind itself consists roughly of an equal number of protons and electrons, also of some heavier ions. These particles travel from the Sun with supersonic velocities and carry away a mass of 1.6 1012 g/s. There are two distinct kind of plasma flow. One is called the fast solar · wind and the other one is called the slow solar wind. The first type has a speed of 400 km/s to 800 km/s and is generated in the so called coronal holes, the dark parts of the corona, which are dominated by open field lines. From these holes open field lines extend, which cannot restrict the speed of the solar wind. In contrast the speed of the second type is only 250 km/s to 400 km/s whereas the density of the slow solar wind is with 8 ions/cm3 higher than the one of the fast solar wind, which is only about 3 ions/cm3 at 1 AU. If there is a solar minimum the slow solar wind comes from regions at the magnetic equator of the sun but during a solar maximum it originates in the streamer belt as it is described in [1]. The coronal streamer belt is limited to a small region of about 20 degrees around the equator. The speed is limited by the closed ± magnetic field lines to which this type of the solar wind is associated to ([6], [7]). One effect on the interplanetary magnetic field emerging from the occurrence of the two different types of the solar wind can be depicted with picture 1.1. As it can be seen in the illustration the sun rotates counterclockwise. Therefore the Archmedian spiral arises. The arrows specify the velocity of 2 1.1. Basic principles 1.1.2 the solar wind. Due to the different velocities of the two types at the transition from the slow solar wind to the fast one a rarefaction of the plasma occurs while a compression results from the approach of the fast one with the slower type.
Recommended publications
  • Contributions to Directional Statistics Based Clustering Methods
    University of California Santa Barbara Contributions to Directional Statistics Based Clustering Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics & Applied Probability by Brian Dru Wainwright Committee in charge: Professor S. Rao Jammalamadaka, Chair Professor Alexander Petersen Professor John Hsu September 2019 The Dissertation of Brian Dru Wainwright is approved. Professor Alexander Petersen Professor John Hsu Professor S. Rao Jammalamadaka, Committee Chair June 2019 Contributions to Directional Statistics Based Clustering Methods Copyright © 2019 by Brian Dru Wainwright iii Dedicated to my loving wife, Carmen Rhodes, without whom none of this would have been possible, and to my sons, Max, Gus, and Judah, who have yet to know a time when their dad didn’t need to work from early till late. And finally, to my mother, Judith Moyer, without your tireless love and support from the beginning, I quite literally wouldn’t be here today. iv Acknowledgements I would like to offer my humble and grateful acknowledgement to all of the wonderful col- laborators I have had the fortune to work with during my graduate education. Much of the impetus for the ideas presented in this dissertation were derived from our work together. In particular, I would like to thank Professor György Terdik, University of Debrecen, Faculty of Informatics, Department of Information Technology. I would also like to thank Professor Saumyadipta Pyne, School of Public Health, University of Pittsburgh, and Mr. Hasnat Ali, L.V. Prasad Eye Institute, Hyderabad, India. I would like to extend a special thank you to Dr Alexander Petersen, who has held the dual role of serving on my Doctoral supervisory commit- tee as well as wearing the hat of collaborator.
    [Show full text]
  • A Guide on Probability Distributions
    powered project A guide on probability distributions R-forge distributions Core Team University Year 2008-2009 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic ditribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 132 13 Misc 134 Conclusion 135 Bibliography 135 A Mathematical tools 138 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗).
    [Show full text]
  • Recent Advances in Directional Statistics
    Recent advances in directional statistics Arthur Pewsey1;3 and Eduardo García-Portugués2 Abstract Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio- temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed. Keywords: Classification; Clustering; Dimension reduction; Distributional
    [Show full text]
  • Multivariate Statistical Functions in R
    Multivariate statistical functions in R Michail T. Tsagris [email protected] College of engineering and technology, American university of the middle east, Egaila, Kuwait Version 6.1 Athens, Nottingham and Abu Halifa (Kuwait) 31 October 2014 Contents 1 Mean vectors 1 1.1 Hotelling’s one-sample T2 test ............................. 1 1.2 Hotelling’s two-sample T2 test ............................ 2 1.3 Two two-sample tests without assuming equality of the covariance matrices . 4 1.4 MANOVA without assuming equality of the covariance matrices . 6 2 Covariance matrices 9 2.1 One sample covariance test .............................. 9 2.2 Multi-sample covariance matrices .......................... 10 2.2.1 Log-likelihood ratio test ............................ 10 2.2.2 Box’s M test ................................... 11 3 Regression, correlation and discriminant analysis 13 3.1 Correlation ........................................ 13 3.1.1 Correlation coefficient confidence intervals and hypothesis testing us- ing Fisher’s transformation .......................... 13 3.1.2 Non-parametric bootstrap hypothesis testing for a zero correlation co- efficient ..................................... 14 3.1.3 Hypothesis testing for two correlation coefficients . 15 3.2 Regression ........................................ 15 3.2.1 Classical multivariate regression ....................... 15 3.2.2 k-NN regression ................................ 17 3.2.3 Kernel regression ................................ 20 3.2.4 Choosing the bandwidth in kernel regression in a very simple way . 23 3.2.5 Principal components regression ....................... 24 3.2.6 Choosing the number of components in principal component regression 26 3.2.7 The spatial median and spatial median regression . 27 3.2.8 Multivariate ridge regression ......................... 29 3.3 Discriminant analysis .................................. 31 3.3.1 Fisher’s linear discriminant function ....................
    [Show full text]
  • Handbook on Probability Distributions
    R powered R-forge project Handbook on probability distributions R-forge distributions Core Team University Year 2009-2010 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic distribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 133 13 Misc 135 Conclusion 137 Bibliography 137 A Mathematical tools 141 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗).
    [Show full text]
  • A New Unified Approach for the Simulation of a Wide Class of Directional Distributions
    This is a repository copy of A New Unified Approach for the Simulation of a Wide Class of Directional Distributions. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/123206/ Version: Accepted Version Article: Kent, JT orcid.org/0000-0002-1861-8349, Ganeiber, AM and Mardia, KV orcid.org/0000-0003-0090-6235 (2018) A New Unified Approach for the Simulation of a Wide Class of Directional Distributions. Journal of Computational and Graphical Statistics, 27 (2). pp. 291-301. ISSN 1061-8600 https://doi.org/10.1080/10618600.2017.1390468 © 2018 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. This is an author produced version of a paper published in Journal of Computational and Graphical Statistics. Uploaded in accordance with the publisher's self-archiving policy. Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ A new unified approach for the simulation of a wide class of directional distributions John T.
    [Show full text]
  • A Logistic Normal Mixture Model for Compositions with Essential Zeros
    A Logistic Normal Mixture Model for Compositions with Essential Zeros Item Type text; Electronic Dissertation Authors Bear, John Stanley Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 28/09/2021 15:07:32 Link to Item http://hdl.handle.net/10150/621758 A Logistic Normal Mixture Model for Compositions with Essential Zeros by John S. Bear A Dissertation Submitted to the Faculty of the Graduate Interdisciplinary Program in Statistics In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy In the Graduate College The University of Arizona 2 0 1 6 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the disserta- tion prepared by John Bear, titled A Logistic Normal Mixture Model for Compositions with Essential Zeros and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. Date: August 18, 2016 Dean Billheimer Date: August 18, 2016 Joe Watkins Date: August 18, 2016 Bonnie LaFleur Date: August 18, 2016 Keisuke Hirano Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.
    [Show full text]
  • High-Precision Computation of Uniform Asymptotic Expansions for Special Functions Guillermo Navas-Palencia
    UNIVERSITAT POLITÈCNICA DE CATALUNYA Department of Computer Science High-precision Computation of Uniform Asymptotic Expansions for Special Functions Guillermo Navas-Palencia Supervisor: Argimiro Arratia Quesada A dissertation submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in Computing May 7, 2019 i Abstract In this dissertation, we investigate new methods to obtain uniform asymptotic ex- pansions for the numerical evaluation of special functions to high-precision. We shall first present the theoretical and computational fundamental aspects required for the development and ultimately implementation of such methods. Applying some of these methods, we obtain efficient new convergent and uniform expan- sions for numerically evaluating the confluent hypergeometric functions 1F1(a; b; z) and U(a, b, z), and the Lerch transcendent F(z, s, a) at high-precision. In addition, we also investigate a new scheme of computation for the generalized exponential integral En(z), obtaining one of the fastest and most robust implementations in double-precision arithmetic. In this work, we aim to combine new developments in asymptotic analysis with fast and effective open-source implementations. These implementations are com- parable and often faster than current open-source and commercial state-of-the-art software for the evaluation of special functions. ii Acknowledgements First, I would like to express my gratitude to my supervisor Argimiro Arratia for his support, encourage and guidance throughout this work, and for letting me choose my research path with full freedom. I also thank his assistance with admin- istrative matters, especially in periods abroad. I am grateful to Javier Segura and Amparo Gil from Universidad de Cantabria for inviting me for a research stay and for their inspirational work in special func- tions, and the Ministerio de Economía, Industria y Competitividad for the financial support, project APCOM (TIN2014-57226-P), during the stay.
    [Show full text]
  • Package 'Directional'
    Package ‘Directional’ May 26, 2021 Type Package Title A Collection of R Functions for Directional Data Analysis Version 5.0 URL Date 2021-05-26 Author Michail Tsagris, Giorgos Athineou, Anamul Sajib, Eli Amson, Micah J. Waldstein Maintainer Michail Tsagris <[email protected]> Description A collection of functions for directional data (including massive data, with millions of ob- servations) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distribu- tions and more are included. The standard textbook for such data is the ``Directional Statis- tics'' by Mardia, K. V. and Jupp, P. E. (2000). Other references include a) Phillip J. Paine, Si- mon P. Preston Michail Tsagris and Andrew T. A. Wood (2018). An elliptically symmetric angu- lar Gaussian distribution. Statistics and Computing 28(3): 689-697. <doi:10.1007/s11222-017- 9756-4>. b) Tsagris M. and Alenazi A. (2019). Comparison of discriminant analysis meth- ods on the sphere. Communications in Statistics: Case Studies, Data Analysis and Applica- tions 5(4):467--491. <doi:10.1080/23737484.2019.1684854>. c) P. J. Paine, S. P. Pre- ston, M. Tsagris and Andrew T. A. Wood (2020). Spherical regression models with general co- variates and anisotropic errors. Statistics and Computing 30(1): 153--165. <doi:10.1007/s11222- 019-09872-2>. License GPL-2 Imports bigstatsr, parallel, doParallel, foreach, RANN, Rfast, Rfast2, rgl RoxygenNote 6.1.1 NeedsCompilation no Repository CRAN Date/Publication 2021-05-26 15:20:02 UTC R topics documented: Directional-package . .4 A test for testing the equality of the concentration parameters for ciruclar data . .5 1 2 R topics documented: Angular central Gaussian random values simulation .
    [Show full text]
  • Spherical-Homoscedastic Distributions: the Equivalency of Spherical and Normal Distributions in Classification
    Journal of Machine Learning Research 8 (2007) 1583-1623 Submitted 4/06; Revised 1/07; Published 7/07 Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification Onur C. Hamsici [email protected] Aleix M. Martinez [email protected] Department of Electrical and Computer Engineering The Ohio State University Columbus, OH 43210, USA Editor: Greg Ridgeway Abstract Many feature representations, as in genomics, describe directional data where all feature vectors share a common norm. In other cases, as in computer vision, a norm or variance normalization step, where all feature vectors are normalized to a common length, is generally used. These repre- sentations and pre-processing step map the original data from Rp to the surface of a hypersphere p 1 S − . Such representations should then be modeled using spherical distributions. However, the difficulty associated with such spherical representations has prompted researchers to model their spherical data using Gaussian distributions instead—as if the data were represented in Rp rather p 1 than S − . This opens the question to whether the classification results calculated with the Gaus- sian approximation are the same as those obtained when using the original spherical distributions. In this paper, we show that in some particular cases (which we named spherical-homoscedastic) the answer to this question is positive. In the more general case however, the answer is negative. For this reason, we further investigate the additional error added by the Gaussian modeling. We conclude that the more the data deviates from spherical-homoscedastic, the less advisable it is to employ the Gaussian approximation.
    [Show full text]
  • Clustering on the Unit Hypersphere Using Von Mises-Fisher Distributions
    JournalofMachineLearningResearch6(2005)1345–1382 Submitted 9/04; Revised 4/05; Published 9/05 Clustering on the Unit Hypersphere using von Mises-Fisher Distributions Arindam Banerjee [email protected] Department of Electrical and Computer Engineering University of Texas at Austin Austin, TX 78712, USA Inderjit S. Dhillon [email protected] Department of Computer Sciences University of Texas at Austin Austin, TX 78712, USA Joydeep Ghosh [email protected] Department of Electrical and Computer Engineering University of Texas at Austin Austin, TX 78712, USA Suvrit Sra [email protected] Department of Computer Sciences University of Texas at Austin Austin, TX 78712, USA Editor: Greg Ridgeway Abstract Several large scale data mining applications, such as text categorization and gene expression anal- ysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a gen- erative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentra- tion parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants.
    [Show full text]
  • A Toolkit for Inference and Learning in Dynamic Bayesian Networks Martin Paluszewski*, Thomas Hamelryck
    Paluszewski and Hamelryck BMC Bioinformatics 2010, 11:126 http://www.biomedcentral.com/1471-2105/11/126 SOFTWARE Open Access Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks Martin Paluszewski*, Thomas Hamelryck Abstract Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations). Results: The program package is freely available under the GNU General Public Licence (GPL) from SourceForge http://sourceforge.net/projects/mocapy. The package contains the source for building the Mocapy++ library, several usage examples and the user manual. Conclusions: Mocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail. Background implementation of Mocapy (T. Hamelryck, University of A Bayesian network (BN) represents a set of variables Copenhagen,2004,unpublished).Today,Mocapyhas and their joint probability distribution using a directed been re-implemented in C++ but the name is kept for acyclic graph [1,2]. A dynamic Bayesian network (DBN) historical reasons. Mocapy supports a large range of is a BN that represents sequences, such as time-series architectures and probability distributions, and has pro- from speech data or biological sequences [3]. One of the ven its value in several published applications [9-13].
    [Show full text]