caa a22 4500 605470227 CHVBK 20210128100326.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00500-014-1428-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1428-0 Using idea of three-step sparse residuals measurement to perform discriminant analysis [Elektronische Daten] [Xiaoning Song, Zi Liu, Jingyu Yang, Xiaojun Wu] Classification of high-dimensional data is usually not amenable to standard pattern recognition techniques owing to lack of necessary, underlying structured information of data. In this paper, we propose a new discriminant analysis based on three-step sparse residuals measurement called DA-TSSR to address this problem. Specifically, in the first stage of the proposed method, the contribution in presenting the test sample of any chosen class is respectively calculated by adding up the total contributions of all the training samples of this class, and then a certain class with the smallest contribution score is eliminated from the set of the training samples. This procedure is iteratively carried out for the set of the training samples of the remaining classes till the predefined termination condition is satisfied. The second stage of DA-TSSR seeks to represent the test sample as a linear combination of all the remaining training samples and exploits the representation ability of each training sample to determine M "nearest neighbors” for the test sample. By this means, it generates unequal number of training samples on each candidate class. The third stage of DA-TSSR again determines a new weighted sum of all unequal numbers of training samples from candidate classes, which is approximately equal to the test sample. We use the new weighted sum to perform the designing of sparse residuals grades, which can be incorporated into the typical discriminant analysis criterion. The proposed method not only has a high accuracy but also can be clearly interpreted. Experimental results conducted on the ORL, XM2VTS, FERET and AR face databases demonstrate the effectiveness of the proposed method. Springer-Verlag Berlin Heidelberg, 2014 Sparse residuals measurement nationallicence Discriminant analysis nationallicence Transform method nationallicence Image recognition nationallicence Song Xiaoning School of Internet of Things Engineering, Jiangnan University, 214122, Wuxi, People's Republic of China aut Liu Zi School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut Yang Jingyu School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut Wu Xiaojun School of Internet of Things Engineering, Jiangnan University, 214122, Wuxi, People's Republic of China aut Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2355-2370 1432-7643 19:8<2355 2015 19 500 https://doi.org/10.1007/s00500-014-1428-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1428-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Song Xiaoning School of Internet of Things Engineering, Jiangnan University, 214122, Wuxi, People's Republic of China aut NATIONALLICENCE 700 1- Liu Zi School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut NATIONALLICENCE 700 1- Yang Jingyu School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut NATIONALLICENCE 700 1- Wu Xiaojun School of Internet of Things Engineering, Jiangnan University, 214122, Wuxi, People's Republic of China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2355-2370 1432-7643 19:8<2355 2015 19 500