NDLI: A new sparse representation-based classification algorithm using iterative class elimination

Content Provider	Springer Nature Link
Author	Song, Xiaoning Liu, Zi Yang, Xibei Gao, Shang
Copyright Year	2013
Abstract	In this paper, we propose a sparse representation-based classification method using iterative class elimination strategy for face recognition. The proposed method aims to represent a test sample as a linear combination of the most competitive training samples and exploits an optimal representation of training samples from the classes with major relevant contributions. We interpret the sparse representation problem as an information fidelity problem. In the context of our proposed method, an important goal is to select a subset of variables for accomplishing one objective: the provision of a descriptive representation for sparse class knowledge structure. We develop an iterative class elimination algorithm to achieve this goal. First, the contribution in presenting the test sample of any of the specified classes is, respectively, calculated by adding up the total contribution of all training samples of this class, and then a certain class that meets the smallest score requirement to this test sample is eliminated. Second, a similar procedure is iteratively carried out for the set of remaining training samples from rest classes, and this procedure is repeatedly performed till the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to classify it. Therefore, the proposed algorithm is an iterative method that alternates between sparse representation and a process of updating the training atoms to better fit the test data. This is helpful to accurately classify the test sample. Experimental results conducted on the ORL, FERET, and AR face databases demonstrate the effectiveness of the proposed method.
Starting Page	1627
Ending Page	1637
Page Count	11
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	24
Issue Number	7-8
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2013-04-06
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sparse representation Image recognition Transform method Classification Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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