Multiple observation sets classification algorithm based on joint dynamic sparse representation of low-rank decomposition

作者：Hu Zheng Ping; Gao Hong Xiao; Zhao Shu Huan

来源：Acta Electronica Sinica, 2015, 43(3): 440-446.

DOI：10.3969/j.issn.0372-2112.2015.03.004

• Classification algorithm
• Classification performance
• Classification results
• Comparative experiments
• Joint dynamic sparsities
• Low-rank decomposition
• Low-rank matrix recoveries
• Sparse representation

Multiple unconstrained observations of the same object can be easily accessed by the Internet, with regard to overcoming the identification-difficult of the unconstrained samples. Moreover, to exploit the information of multiple observation sets to improve the classification performance, a multiple observation sets classification algorithm based on joint dynamic spare representation of low-rank decomposition is presented. First of all, we need find the best set of image transform domain, which decomposes the data matrix into a low-rank matrix and an associated sparse error matrix. Secondly, the low-rank matrix and sparse error matrix is represented by joint dynamic sparsity respectively, in order to make full use of the correlation of the class-level and the differences of the atom-level, i. e, the sparse representation vectors for the multiple observations can share the same class-level sparsity pattern while their atom-level sparsity patterns may be distinct. Finally, we compare the classification results with the total sparse reconstruction errors. Three comparative experiments are conducted on CMU-PIE face dataset, ETH-80 object recognition dataset, USPS handwritten digit dataset, and UMIST face dataset, and the results demonstrate the superiority of the proposed algorithm.

• 出版日期2015