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A proximal point algorithm for sequential feature extraction applications

Xuan Vinh Doan(vanxuan***at***uwaterloo.ca)
Kim-Chuan Toh(mattohkc***at***nus.edu.sg)
Stephen Vavasis(vavasis***at***math.uwaterloo.ca)

Abstract: We propose a proximal point algorithm to solve 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 a new stopping criterion for the proximal point algorithm, which is based on the duality conditions of \eps-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.

Keywords: feature extraction, low-rank optimization, proximal point algorithm

Category 1: Applications -- Science and Engineering (Data-Mining )

Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation:

Download: [PDF]

Entry Submitted: 08/03/2011
Entry Accepted: 08/03/2011
Entry Last Modified: 08/03/2011

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