Optimization Online


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 )


Download: [PDF]

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

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society