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Combine the clustering algorithm and representation-based algorithm for concurrent classification of test samples

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2 Author(s)
Xiao-Zhao Fang ; Bio-Comput. Res. Center, Harbin Inst. of Technol., Shenzhen, China ; Yong Xu

Sparse representation (SR) is a novel pattern recognition method. The algorithm of SR usually performs well. However, in processing a massive concurrent recognition task, SR has a very high computational cost because every test sample has to seek to an optimal linear combination of all the training samples. To this end, we propose a novel method which can perform well without needing to seek a linear combination of all the training samples for every test sample. Our proposed method can be divided into two steps: the first step of the proposed method uses c-means clustering to categorize the test sets into c subsets and then calculates K nearest neighbors for each class centre from all the training samples. The second step represents test samples located in each subset as a linear combination of the according K nearest neighbors and uses representation result to perform ultimate classification. A large number of experimental results show that the proposed algorithm is promising.

Published in:

Computational Intelligence for Security and Defence Applications (CISDA), 2012 IEEE Symposium on

Date of Conference:

11-13 July 2012

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