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Title: A self-representation induced classifier
Authors: Zhu, P
Lei, Z 
Zuo, W
Xiangchu, F
Hu, Q
Issue Date: 2016
Publisher: International Joint Conferences on Artificial Intelligence
Source: IJCAI International Joint Conference on Artificial Intelligence, 2016, v. 2016-January, p. 2442-2448 How to cite?
Abstract: Almost all the existing representation based classifiers represent a query sample as a linear combination of training samples, and their time and memory cost will increase rapidly with the number of training samples. We investigate the representation based classification problem from a rather different perspective in this paper, that is, we learn how each feature (i.e., each element) of a sample can be represented by the features of itself. Such a self-representation property of sample features can be readily employed for pattern classification and a novel self-representation induced classifier (SRIC) is proposed. SRIC learns a selfrepresentation matrix for each class. Given a query sample, its self-representation residual can be computed by each of the learned self-representation matrices, and classification can then be performed by comparing these residuals. In light of the principle of SRIC, a discriminative SRIC (DSRIC) method is developed. For each class, a discriminative selfrepresentation matrix is trained to minimize the self-representation residual of this class while representing little the features of other classes. Experimental results on different pattern recognition tasks show that DSRIC achieves comparable or superior recognition rate to state-of-the-art representation based classifiers, however, it is much more efficient and needs much less storage space.
Description: 25th International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, US, 9-15 July 2016
ISSN: 1045-0823
Appears in Collections:Conference Paper

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