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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Srivastava, G. Ming Shao Yun Fu |
| Copyright Year | 2013 |
| Description | Author affiliation: Coll. of Comput. & Inf. Sci., Northeastern Univ., Boston, MA, USA (Ming Shao) || ECE Dept., Northeastern Univ., Boston, MA, USA (Yun Fu) || Samsung Res. America, Richardson, TX, USA (Srivastava, G.) |
| Abstract | In this paper, we describe a novel semisupervised method for face classification using a low-rank subspace embedding. We demonstrate our approach through the examples of multiclass and multilabel learning applied to face classification. In the past, supervised embedding approaches have been devised where only the labeled data are utilized to seek a low-dimensional subspace such that the instances belonging to the same class or having similar multilabels are clustered together in this subspace. Our main contribution is to extend such approaches to semisupervised domain by introducing a low-rank linear constraint between the labeled and unlabeled data during the learning process. This constraint enables the unlabeled data also to be clustered similarly to the labeled data. The Low Rank Representation (LRR) has been recently investigated by several researchers due to its robust subspace segmentation property. The advantages of the proposed approach are confirmed through extensive experiments. |
| Sponsorship | IEEE Biomet. Counc. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 2834055 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467355452 |
| e-ISBN | 9781467355469 |
| e-ISBN | 9781467355445 |
| DOI | 10.1109/FG.2013.6553704 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-04-22 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face Accuracy Training Semisupervised learning Databases Robustness Vectors |
| Content Type | Text |
| Resource Type | Article |
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