An introduction to computational learning theory
An introduction to computational learning theory
Randomized algorithms
Learning mixtures of arbitrary gaussians
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Logconcave Functions: Geometry and Efficient Sampling Algorithms
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
A spectral algorithm for learning mixture models
Journal of Computer and System Sciences - Special issue on FOCS 2002
A two-round variant of EM for Gaussian mixtures
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
An investigation of computational and informational limits in Gaussian mixture clustering
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Unified Continuous Optimization Framework for Center-Based Clustering Methods
The Journal of Machine Learning Research
A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians
The Journal of Machine Learning Research
Spectral clustering with limited independence
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A discriminative framework for clustering via similarity functions
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Data spectroscopy: learning mixture models using eigenspaces of convolution operators
Proceedings of the 25th international conference on Machine learning
Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in ${\mathbb R}^d$
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Clustering with Interactive Feedback
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Approximate clustering without the approximation
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Multiple pass streaming algorithms for learning mixtures of distributions in Rd
Theoretical Computer Science
Multi-view clustering via canonical correlation analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions
APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Separating populations with wide data: a spectral analysis
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
PAC learning axis-aligned mixtures of gaussians with no separation assumption
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Fast mining and forecasting of complex time-stamped events
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering under approximation stability
Journal of the ACM (JACM)
| Hi-index | 0.00 |
We present an algorithm for learning a mixture of distributions based on spectral projection. We prove a general property of spectral projection for arbitrary mixtures and show that the resulting algorithm is efficient when the components of the mixture are logconcave distributions in $\Re^{n}$ whose means are separated. The separation required grows with k, the number of components, and log n. This is the first result demonstrating the benefit of spectral projection for general Gaussians and widens the scope of this method. It improves substantially on previous results, which focus either on the special case of spherical Gaussians or require a separation that has a considerably larger dependence on n.