Parallel Approximate Matrix Factorization for Kernel Methods
Kaihua Zhu
Hang Cui
Hongjie Bai
Jian Li
Zhihuan Qiu
Hao Wang
Hui Xu
Chang, E.Y.
This paper appears in: Multimedia and Expo, 2007 IEEE International Conference on
Publication Date: 2-5 July 2007
On page(s): 1275-1278
Location: Beijing,
ISBN: 1-4244-1016-9
INSPEC Accession Number: 9804507
Digital Object Identifier: 10.1109/ICME.2007.4284890
Current Version Published: 2007-08-08
Abstract
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.
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