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    188宝金博页面版: Variational learning of clusters of undercomplete nonsymmetric independent components

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    内容提示： Journal of Machine Learning Research 3 (2002) 99-114 Submitted 5/02; Published 8/02Variational Learning of Clusters of Undercomplete NonsymmetricIndependent ComponentsKwokleung Chan KWCHAN @ SALK . EDUComputational Neurobiology LaboratoryThe Salk Institute10010 North Torrey Pines RoadLa Jolla, CA 92037, USATe-Won Lee TEWON @ SALK . EDUInstitute for Neural ComputationUniversity of California at San DiegoLa Jolla, CA 92093, USATerrence J. Sejnowski TERRY @ SALK . EDUComputational Neurobiology LaboratoryThe S...

    文档格式：PDF | 页数：16 | 浏览次数：22 | 上传日期：2017-04-12 11:54:50 | 文档星级：
    Journal of Machine Learning Research 3 (2002) 99-114 Submitted 5/02; Published 8/02Variational Learning of Clusters of Undercomplete NonsymmetricIndependent ComponentsKwokleung Chan KWCHAN @ SALK . EDUComputational Neurobiology LaboratoryThe Salk Institute10010 North Torrey Pines RoadLa Jolla, CA 92037, USATe-Won Lee TEWON @ SALK . EDUInstitute for Neural ComputationUniversity of California at San DiegoLa Jolla, CA 92093, USATerrence J. Sejnowski TERRY @ SALK . EDUComputational Neurobiology LaboratoryThe Salk Institute10010 North Torrey Pines RoadLa Jolla, CA 92037, USAandDepartment of BiologyUniversity of California at San DiegoLa Jolla, CA 92093, USAEditor: Michael I. JordanAbstractWe apply a variational method to automatically determine the number of mixtures of indepen-dent components in high-dimensional datasets, in which the sources may be nonsymmetricallydistributed. The data are modeled by clusters where each cluster is described as a linear mixtureof independent factors. The variational Bayesian method yields an accurate density model for theobserved data without overf i tting problems. This allows the dimensionality of the data to be iden-tif i ed for each cluster. The new method was successfully applied to a diff i cult real-world medicaldataset for diagnosing glaucoma.Keywords: Density Estimations, Mixture Models, Bayesian Learning, ICA1. IntroductionThe performance of a method for pattern classif i cation is often determined by how well it canmodel the underlying statistical distribution of the data. Independent component analysis (ICA)models non-Gaussian structure, e.g., platykurtic or leptokurtic probability density functions. In ICA(Hyvarinen et al., 2001), the observed data x are assumed to be generated from a linear combinationof independent sources s:x = As + ν ,c ?2002 Kwokleung Chan, Te-Won Lee and Terrence J. Sejnowski.

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