Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition | Zendy

Chang Liu | Zendy; Tao Yan | Zendy; Weidong Zhao | Zendy; Yonghong Liu | Zendy; Dan Li | Zendy; Feng Lin | Zendy; Jiliu Zhou | Zendy

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Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

Author(s) -

Chang Liu,

Tao Yan,

Weidong Zhao,

Yonghong Liu,

Dan Li,

Feng Lin,

Jiliu Zhou

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/819758

Subject(s) - principal component analysis , dimensionality reduction , pattern recognition (psychology) , tensor (intrinsic definition) , artificial intelligence , sparse pca , computer science , curse of dimensionality , singular value decomposition , embedding , graph , mathematics , theoretical computer science , pure mathematics

To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA) based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA), incremental principal component analysis (IPCA), and multilinear principal component analysis (MPCA) algorithms. At the same time, ITPCA also has lower time and space complexity

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