Semi-supervised classification via discriminative sparse manifold regularization |
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| Affiliation: | 1. School of Automation Science and Electrical Engineering Beihang University, Beijing, China;2. Computer Science and Digital Technologies Department, Northumbria University, Newcastle, UK;3. Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China;4. School of Computer & Software, Nanjing University of Information Science & Technology, China;1. University of Southern California, Los Angeles, CA, USA;2. National Taiwan University, Taipei, Taiwan;1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China;2. Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, China;3. Microsoft Research, Beijing 100000, China |
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| Abstract: | In this paper, a newly semi-supervised manifold learning algorithm named Discriminative Sparse Manifold Regularization (DSMR) is proposed. In DSMR, the whole unlabeled sample set is used to reconstruct the mean vector of each class, then obtains the sparse coefficient. For each sample of labeled samples, the new dictionary is composed of samples from the same class and the samples from the unlabeled sample set according to the corresponding rows of the sparse coefficient. For each unlabeled sample, the new dictionary is composed of samples from the whole unlabeled samples and the samples from the labeled class according to the corresponding columns of the sparse coefficient. Additionally, a discriminative term is added to stabilize performance of the algorithm. Extensive experiments on the several UCI datasets and face datasets demonstrate the effectiveness of the proposed DSMR. |
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| Keywords: | Manifold regularization Semi-supervised learning Classification Sparse representation |
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