Incremental Tensor Principal Component Analysis for Image Recognition

Abstract:

Aiming at the disadvantages of the traditional off-line vector-based learning algorithm, this paper proposes a kind of Incremental Tensor Principal Component Analysis (ITPCA) algorithm. It represents an image as a tensor data and processes incremental principal component analysis learning based on update-SVD technique. On the one hand, the proposed algorithm is helpful to preserve the structure information of the image. On the other hand, it solves the training problem for new samples. The experiments on handwritten numeral recognition have demonstrated that the algorithm has achieved better performance than traditional vector-based Incremental Principal Component Analysis (IPCA) and Multi-linear Principal Component Analysis (MPCA) algorithms.

• Periodical: Advanced Materials Research (Volume 710)
• Pages: 584-588
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.710.584
• Online since: June 2013
• Authors: Wei Dong Zhao, Chang Liu, Tao Yan
• Keywords: Image Recognition, Incremental Learning, Principal Component Analysis (PCA), Tensor Analysis

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