A Proximal Point Algorithm for Sequential Feature Extraction Applications

نویسندگان

  • Xuan Vinh Doan
  • Kim-Chuan Toh
  • Stephen A. Vavasis
چکیده

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 ǫ-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.

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عنوان ژورنال:
  • SIAM J. Scientific Computing

دوره 35  شماره 

صفحات  -

تاریخ انتشار 2013