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A proximal point algorithm for sequential feature extraction applications
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Doan, Xuan Vinh, Toh, Kim-Chuan and Vavasis, Stephen (2013) A proximal point algorithm for sequential feature extraction applications. SIAM Journal on Scientific Computing, Volume 35 (Number 1). A517-A540. doi:10.1137/110843381 ISSN 1064-8275.
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Official URL: http://dx.doi.org/10.1137/110843381
Abstract
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.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
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| Journal or Publication Title: | SIAM Journal on Scientific Computing | ||||
| Publisher: | Society for Industrial and Applied Mathematics | ||||
| ISSN: | 1064-8275 | ||||
| Official Date: | 2013 | ||||
| Dates: |
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| Volume: | Volume 35 | ||||
| Number: | Number 1 | ||||
| Page Range: | A517-A540 | ||||
| DOI: | 10.1137/110843381 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access |
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