Title :
Combine the clustering algorithm and representation-based algorithm for concurrent classification of test samples
Author :
Fang, Xiao-Zhao ; Xu, Yong
Author_Institution :
Bio-Comput. Res. Center, Harbin Inst. of Technol., Shenzhen, China
Abstract :
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.
Keywords :
pattern classification; pattern clustering; SR algorithm; c subsets; c-means clustering; clustering algorithm; k nearest neighbors; pattern recognition method; representation-based algorithm; sparse representation; test samples concurrent classification; training samples optimal linear combination; Classification algorithms; Clustering algorithms; Computational efficiency; Databases; Face; Pattern recognition; Training; Clutering method; Linear combination; Nearest neighbors; Pattern recognition; Sparse representation;
Conference_Titel :
Computational Intelligence for Security and Defence Applications (CISDA), 2012 IEEE Symposium on
Conference_Location :
Ottawa, ON
Print_ISBN :
978-1-4673-1416-9
DOI :
10.1109/CISDA.2012.6291514