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Large-Scale Nystrom Kernel Matrix Approximation Using Randomized SVD
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DOI：10.1109/TNNLS.2014.2359798.png)
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Abstract
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The Nystrom method is an efficient technique for the eigenvalue decomposition of large kernel matrices. However, to ensure an accurate approximation, a sufficient number of columns have to be sampled. On very large data sets, the singular value decomposition (SVD) step on the resultant data submatrix can quickly dominate the computations and become prohibitive. In this paper, we propose 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 using the recent randomized low-rank matrix approximation algorithms. Theoretical analysis shows that the proposed algorithm is as accurate as the standard Nystrom method that directly performs a large SVD on the inner submatrix. On the other hand, its time complexity is only as low as performing a small SVD. Encouraging results are obtained on a number of large-scale data sets for low-rank approximation. Moreover, as the most computational expensive steps can be easily distributed and there is minimal data transfer among the processors, significant speedup can be further obtained with the use of multiprocessor and multi-GPU systems.
Keywords:
Distributed computing
graphics processor
large-scale learning
low-rank matrix approximation
Nystrom method
randomized SVD
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