A PROXIMAL POINT ALGORITHM FOR SEQUENTIAL FEATURE EXTRACTION APPLICATIONS

doi:10.1137/110843381

A PROXIMAL POINT ALGORITHM FOR SEQUENTIAL FEATURE EXTRACTION APPLICATIONS

2013-01-01

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DOI：10.1137/110843381

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We propose a proximal point algorithm to solve the LAROS problem, that is, the problem of finding a large approximately rank-one submatrix. This LAROS problem is used to sequentially extract features in data. We also develop new stopping criteria for the proximal point algorithm, which is based on the duality conditions of epsilon-optimal solutions of the LAROS problem, with a theoretical guarantee. We test our algorithm with two image databases and show that we can use the LAROS problem to extract appropriate common features from these images.

Keyword:

proximal point algorithm

nuclear norm

feature extraction

A PROXIMAL POINT ALGORITHM FOR SEQUENTIAL FEATURE EXTRACTION APPLICATIONS

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