The spectral norm error of the naive Nystrom extension
@article{Gittens2011TheSN, title={The spectral norm error of the naive Nystrom extension}, author={Alex Gittens}, journal={ArXiv}, year={2011}, volume={abs/1110.5305} }
The naive Nystrom extension forms a low-rank approximation to a positive-semidefinite matrix by uniformly randomly sampling from its columns. This paper provides the first relative-error bound on the spectral norm error incurred in this process. This bound follows from a natural connection between the Nystrom extension and the column subset selection problem. The main tool is a matrix Chernoff bound for sampling without replacement.
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References
SHOWING 1-10 OF 20 REFERENCES
A novel greedy algorithm for Nyström approximation
- Computer ScienceAISTATS
- 2011
A novel recursive algorithm for calculating the Nystrom approximation, and an effective greedy criterion for column selection are presented, and a very efficient variant is proposed for greedy sampling, which works on random partitions of data instances.
Spectral approximations in machine learning
- Computer Science
- 2011
Two methods for reducing the computational burden of spectral decompositions are discussed: the more venerable Nystom extension and a newly introduced algorithm based on random projections.
On sampling-based approximate spectral decomposition
- Computer ScienceICML '09
- 2009
This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications and proposes an efficient adaptive sampling technique to select informative columns from the original matrix.
Matrix Coherence and the Nystrom Method
- Computer ScienceUAI
- 2010
This work derives novel coherence-based bounds for the Nystrom method in the low-rank setting and presents empirical results that corroborate these theoretical bounds and convincingly demonstrate the ability of matrix coherence to measure the degree to which information can be extracted from a subset of columns.
Making Large-Scale Nyström Approximation Possible
- Computer ScienceICML
- 2010
An accurate and scalable Nystrom scheme that first samples a large column subset from the input matrix, but then only performs an approximate SVD on the inner submatrix by using the recent randomized low-rank matrix approximation algorithms.
Sampling Techniques for the Nystrom Method
- Computer ScienceAISTATS
- 2009
This work presents novel experiments with several real world datasets, and suggests that uniform sampling without replacement, in addition to being more efficient both in time and space, produces more effective approximations.
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
- Computer Science, MathematicsJ. Mach. Learn. Res.
- 2005
An algorithm to compute an easily-interpretable low-rank approximation to an n x n Gram matrix G such that computations of interest may be performed more rapidly.
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
- Computer ScienceSIAM Rev.
- 2011
This work surveys and extends recent research which demonstrates that randomization offers a powerful tool for performing low-rank matrix approximation, and presents a modular framework for constructing randomized algorithms that compute partial matrix decompositions.
Spectral methods in machine learning and new strategies for very large datasets
- Computer ScienceProceedings of the National Academy of Sciences
- 2009
Two new algorithms for the approximation of positive-semidefinite kernels based on the Nyström method are presented, each of which demonstrates the improved performance of the approach relative to existing methods.
Improved Analysis of the subsampled Randomized Hadamard Transform
- Computer Science, MathematicsAdv. Data Sci. Adapt. Anal.
- 2011
An improved analysis of a structured dimension-reduction map called the subsampled randomized Hadamard transform is presented, and it offers optimal constants in the estimate on the number of dimensions required for the embedding.