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Parallel Approximate Matrix Factorization for Kernel Methods

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8 Author(s)

The kernel methods play a pivotal role in machine learning algorithms. Unfortunately, working with the kernel methods must deal with an n times n kernel matrix, which is memory intensive. In this paper, we present a parallel, approximate matrix factorization algorithm, which loads only essential data to individual processors to enable parallel processing of data. Our method reduces space requirement for the kernel matrix from O(n2) to O(np/m), where n is the amount of data, p the reduced matrix dimension (p << n), and m the number of processors.

Published in:

2007 IEEE International Conference on Multimedia and Expo

Date of Conference:

2-5 July 2007