Low rank approximation

Description

A common problem in many areas of large-scale machine learning involves deriving a useful and efficient approximation of a large matrix. This matrix may be the Gram matrix associated to a positive definite kernel in kernel-based algorithms in classification, dimensionality reduction, or some other large matrix arising in other learning tasks such as clustering, collaborative filtering, or matrix completion. For these large-scale problems, the number of matrix entries can be in the order of tens of thousands to millions.

Related Publications

Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar.
Sampling methods for the Nyström method.
Journal of Machine Learning Research (JMLR), to appear, 2012.

Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar.
Ensemble Nyström.
In Cha Zhang and Yunqian Ma, editors, Ensemble Machine Learning. pages 203-223. Springer, 2012.

Mehryar Mohri and Ameet Talwalkar.
Can matrix coherence be efficiently and accurately estimated?.
In Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2011). Ft. Lauderdale, FL, April 2011.

Corinna Cortes, Mehryar Mohri, and Ameet Talwalkar.
On the impact of kernel approximation on learning accuracy.
In Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010). Sardinia, Italy, May 2010.

Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar.
Ensemble Nyström method.
In Advances in Neural Information Processing Systems (NIPS 2009). Vancouver, Canada, 2009. MIT Press.

Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar.
On sampling-based approximate spectral decomposition.
In Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML 2009). Montréal, Canada, June 2009.

Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar.
Sampling techniques for the Nyström method.
In Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009). pages 304-311, Clearwater Beach, Florida, April 2009.

	Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar. Sampling methods for the Nyström method. Journal of Machine Learning Research (JMLR), to appear, 2012.
	Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar. Ensemble Nyström. In Cha Zhang and Yunqian Ma, editors, Ensemble Machine Learning. pages 203-223. Springer, 2012.
	Mehryar Mohri and Ameet Talwalkar. Can matrix coherence be efficiently and accurately estimated?. In Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2011). Ft. Lauderdale, FL, April 2011.
	Corinna Cortes, Mehryar Mohri, and Ameet Talwalkar. On the impact of kernel approximation on learning accuracy. In Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010). Sardinia, Italy, May 2010.
	Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar. Ensemble Nyström method. In Advances in Neural Information Processing Systems (NIPS 2009). Vancouver, Canada, 2009. MIT Press.
	Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar. On sampling-based approximate spectral decomposition. In Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML 2009). Montréal, Canada, June 2009.
	Sanjiv Kumar, Mehryar Mohri, and Ameet Talwalkar. Sampling techniques for the Nyström method. In Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009). pages 304-311, Clearwater Beach, Florida, April 2009.