Learning Robust and Discriminative Subspace With Low-Rank Constraints
dc.contributor.author | Sheng Li | |
dc.contributor.author | Yun Fu | |
dc.date.accessioned | 2017-03-27T23:29:51Z | |
dc.date.available | 2017-03-27T23:29:51Z | |
dc.date.issued | 2016 | |
dc.description | IEEE Transactions on Neural Networks and Learning Systems | en_US |
dc.description | The article of record as published may be found at http://dx.doi.org/10.1109/tnnls.2015.2464090 | en_US |
dc.description.abstract | In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classifica- tion. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularization- based robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The exper- imental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations. | en_US |
dc.description.sponsorship | Funded by Naval Postgraduate School | en_US |
dc.description.sponsorship | National Science Foundation Computer and Network Systems | en_US |
dc.description.sponsorship | ONR Young Investigator | en_US |
dc.description.sponsorship | Office of Naval Research | en_US |
dc.description.sponsorship | U.S. Army Research Office Young Investigator | en_US |
dc.identifier.uri | https://hdl.handle.net/10945/52406 | |
dc.relation.ispartofseries | Funded by Naval Postgraduate School | |
dc.rights | Copyright is reserved by the copyright owner. | en_US |
dc.title | Learning Robust and Discriminative Subspace With Low-Rank Constraints | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication | |
relation.isSeriesOfPublication | af4f559a-d2da-4fa4-89d1-78532cf33472 | |
relation.isSeriesOfPublication.latestForDiscovery | af4f559a-d2da-4fa4-89d1-78532cf33472 |
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