2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-MDSP.7
Paper Title 2DTPCA: A NEW FRAMEWORK FOR MULTILINEAR PRINCIPAL COMPONENT ANALYSIS
Authors Cagri Ozdemir, Randy Hoover, Kyle Caudle, South Dakota Mines, United States
SessionMLR-APPL-MDSP: Machine learning for multidimensional signal processing
LocationArea F
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for multidimensional signal processing
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Abstract Two-directional two-dimensional principal component analysis ((2D)$^2$PCA) has shown promising results for it's ability to both represent and recognize facial images. The current paper extends these results into a multilinear framework (referred to as two-directional Tensor PCA or 2DTPCA for short) using a recently defined tensor operator for 3$^\text{rd}$-order tensors. The approach proceeds by first computing a low-dimensional projection tensor for the row-space of the image data (generally referred to as mode-1) and then subsequently computing a low-dimensional projection tensor for the column space of the image data (generally referred to as mode-3). Experimental results are presented on the ORL, extended Yale-B, COIL-100, and MNIST data sets that show the proposed approach outperforms traditional ``tensor-based" PCA approaches with a much smaller subspace dimension in terms of recognition rates.