期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 29, 期 11, 页码 5228-5241出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2796133
关键词
Computer vision; feature extraction; low-rank representation (LRR); pattern recognition; ridge regression
类别
资金
- National Natural Science Foundation of China [61573248, 61772141, 61375012, 61773328, 61703283, 61761130079]
- Hong Kong Polytechnic University [1-YW1C]
- Research Grant of The Hong Kong Polytechnic University [G-YBD9]
- Key Research Program of Frontier Sciences, CAS [QYZDY-SSW-JSC044]
- Guangdong Provincial Natural Science Foundation [17ZK0422]
- Guangdong Science and Technology Planning Project [2013B091300009, 2016B030308001]
- Guangzhou Science and Technology Planning Project [201604046017, 201604030034]
Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised feature extraction method, latent low-rank representation (LatLRR) has shown its power in extracting salient features. However, LatLRR has the following three disadvantages: 1) the dimension of features obtained using LatLRR cannot be reduced, which is not preferred in feature extraction; 2) two low-rank matrices are separately learned so that the overall optimality may not be guaranteed; and 3) LatLRR is an unsupervised method, which by far has not been extended to the supervised scenario. To this end, in this paper, we first propose to use two different matrices to approximate the low-rank projection in LatLRR so that the dimension of obtained features can be reduced, which is more flexible than original LatLRR. Then, we treat the two low-rank matrices in LatLRR as a whole in the process of learning. In this way, they can be boosted mutually so that the obtained projection can extract more discriminative features. Finally, we extend LatLRR to the supervised scenario by integrating feature extraction with the ridge regression. Thus, the process of feature extraction is closely related to the classification so that the extracted features are discriminative. Extensive experiments are conducted on different databases for unsupervised and supervised feature extraction, and very encouraging results are achieved in comparison with many state-of-the-arts methods.
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