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Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

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  • Chang Liu
  • Tao Yan
  • WeiDong Zhao
  • YongHong Liu
  • Dan Li
  • Feng Lin
  • JiLiu Zhou

Abstract

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.

Suggested Citation

  • Chang Liu & Tao Yan & WeiDong Zhao & YongHong Liu & Dan Li & Feng Lin & JiLiu Zhou, 2014. "Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, January.
    • Handle: RePEc:hin:jnlmpe:819758
    DOI: 10.1155/2014/819758
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