Data spectroscopy: Learning mixture models using eigenspaces of convolution operators

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 936-943

  Shi, Tao ^a   Belkin, Mikhail ^b   Yu, Bin ^c  

^a OHIO STATE UNIVERSITY   (United States)

^b Ohio State University

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS.; CONVOLUTION OPERATORS; EIGENSPACES; EIGENVALUES AND EIGENVECTORS; GAUSSIAN MIXTURE MODELS; GAUSSIAN MIXTURES; KERNEL FUNCTIONS; KERNEL MATRIXES; MIXTURE COMPONENTS; MIXTURE DISTRIBUTIONS; MIXTURE MODELS; PARAMETRIC DISTRIBUTIONS; PRINCIPAL COMPONENTS ANALYSES; SAMPLED DATUMS; SPECTRAL CLUSTERING; SPECTRAL FRAMEWORKS; SPECTRAL TECHNIQUES; COVARIANCE MATRICES; KERNEL MATRICES; PRINCIPAL COMPONENTS; SAMPLED DATUM; THEORETICAL FRAMEWORKS;

COMMUNICATION CHANNELS (INFORMATION THEORY); COVARIANCE MATRIX; LEARNING SYSTEMS; OBJECT RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; ROBOT LEARNING; TRELLIS CODES;

MIXTURES;

EID: 56449097024     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. M. BELKIN, P. NIYOGI, Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering, NIPS 2001.
2. S. DASGUPTA, Learning mixtures of Gaussians, FOCS 1999.
3. A. DEMPSTER, N. LAIRD, D. RUBIN, Maximum-likelihood from incomplete data via the em algorithm, Journal of Royal Statistics Society, Ser. B, 39 (1997), pp. 1-38.
4. R.KANNAN, H.SALMASIAN, S.VEMPALA, The spectral method for general mixture models, COLT 2005.
5. T. KATO, Perturbation, Theory for Linear Operators, Springer-Verlag, 1966.
6. V. KOLTCHINSKII AND E. GINÉ, Random matrix approximation of spectra of integral operators, Bernoulli, 6 (2000), pp. 113-167.
7. S. MACEACHERN AND P. MULLER, Estimating mixt are of Dirichlet process models, Journal of Computational and Graphical Statistics, 7 (1998), pp. 223-238.
8. A. NG, M. JORDAN, AND Y. EISS, On spectral clustering: Analysis and an algorithm, NIPS 2001.
9. K. PEARSON, Contributions to the mathematical theory of evolution, Phil. Trans. Royal Soc., 185A, (1894), pp. 71-110.
10. R. REDNER AND H. ALKER, Mixture densities, maximum likelihood and the em algorithm, SIAM Review, 26 (1984), pp. 195-239.
11. B. SCHÖLKOPF, A. J. SMOLA, AND K.-R. MÜLLER, Kernel principal component analysis, Advances in kernel methods: support vector learning, (1999), pp. 327-352.
12. C. ILLIAMS, M.SEEGER, The effect of the input density distribution on kernel-based classifiers, ICML2000.
13. H. ZHU, C. ILLIAMS, R. ROHWER, AND M. MORCINIE, Gaussian regression and optimal finite dimensional linear models, in Neural networks and machine learning, C. Bishop, ed., 1998.