Multiple pattern classification by sparse subspace decomposition
Computer Vision and Pattern Recognition
2016-11-17 v2
Abstract
A robust classification method is developed on the basis of sparse subspace decomposition. This method tries to decompose a mixture of subspaces of unlabeled data (queries) into class subspaces as few as possible. Each query is classified into the class whose subspace significantly contributes to the decomposed subspace. Multiple queries from different classes can be simultaneously classified into their respective classes. A practical greedy algorithm of the sparse subspace decomposition is designed for the classification. The present method achieves high recognition rate and robust performance exploiting joint sparsity.
Cite
@article{arxiv.0907.5321,
title = {Multiple pattern classification by sparse subspace decomposition},
author = {Tomoya Sakai},
journal= {arXiv preprint arXiv:0907.5321},
year = {2016}
}
Comments
8 pages, 3 figures, 2nd IEEE International Workshop on Subspace Methods, Workshop Proceedings of ICCV 2009