A proximal point algorithm for sequential feature extraction applications
Optimization and Control
2011-08-05 v1
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.
Cite
@article{arxiv.1108.0986,
title = {A proximal point algorithm for sequential feature extraction applications},
author = {Xuan Vinh Doan and Kim-Chuan Toh and Stephen Vavasis},
journal= {arXiv preprint arXiv:1108.0986},
year = {2011}
}