Please use this identifier to cite or link to this item: https://doi.org/10.1137/110843381
Title: A proximal point algorithm for sequential feature extraction applications
Authors: Doan, X.V.
Toh, K.-C. 
Vavasis, S.
Keywords: Feature extraction
Nuclear norm
Proximal point algorithm
Issue Date: 2013
Source: Doan, X.V., Toh, K.-C., Vavasis, S. (2013). A proximal point algorithm for sequential feature extraction applications. SIAM Journal on Scientific Computing 35 (1) : A517-A540. ScholarBank@NUS Repository. https://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 ε-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. © 2013 Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Scientific Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/102738
ISSN: 10648275
DOI: 10.1137/110843381
Appears in Collections:Staff Publications

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