An investigation of computational and informational limits in Gaussian mixture clustering

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 865-872

  Srebro, Nathan ^a   Shakhnarovich, Gregory ^b   Roweis, Sam ^a  

^a UNIVERSITY OF TORONTO   (Canada)

^b Brown University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; INFORMATION ANALYSIS; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS;

COMPUTATIONAL RESTRICTIONS; GAUSSIAN MIXTURE; INFORMATIONAL LIMITS;

CLUSTERING ALGORITHMS;

EID: 34250705300     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143953     Document Type: Conference Paper

References (10)

1. Achlioptas, D., & McSherry, F. (2005). On spectral learning of mixtures of distributions. 18th Annual Conference on Learning Theory (COLT).
2. Arora, S., & Kannan, R. (2001). Learning mixtures of arbitrary gaussians. Proceedings of the thirty-third annual ACM symposium on Theory of computing.
3. Barkai, N., & Sompolinksy, H. (1994). Statistical mechanics of maximum-likelihood desnsity estimation. Physical Review E, 50, 1766-1769.
4. Dasgupta, S. (1999). Learning mixtures of gaussians. Proceedings of the 40th Annual Symposium on Foundations of Computer Science.
5. Dasgupta, S., & Schulman, L. (2000). A two-round variant of em for gaussian mixtures. Proceedings of the 16th Annual Conference on Uncertainty in Artificial Intelligence.
6. Kannan, R., Salmasian, H., & Vempala, S. (2005). The spectral method for general mixture models. 18th Annual Conference on Learning Theory (COLT).
7. Kumar, A., Sabharwal, Y., & Sen, S. (2004). A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimensions. Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science (FOCS).
8. Redner, R. A., & Walker, H. F. (1984). Mixture densities, maximum likelihood and the EM algorithm. SIAM Review, 26, 195-239.
9. Vempala, S., & Wang, G. (2004). A spectral algorithm for learning mixture models. J. Comput. Syst. Sci., 68, 841-860.
10. Watkins, T., & Nadal, J. (1994). Optimal unsupervised learning. J. Phys. A, 27, 1899-1915.