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Although widely used, Multilinear PCA (MPCA), one of the leading multilinear analysis methods, still suffers from four major drawbacks. First, it is very sensitive to outliers and noise. Second, it is unable to cope with missing values. Third, it is computationally expensive since MPCA deals with large multi-dimensional datasets. Finally, it is unable to maintain the local geometrical structures due...
Face recognition under various conditions such as illumination, poses, expression, and occlusion has been one of the most challenging problems in computer vision. Over the last few years there has been significant attention paid to the low-rank approximation (LRA) and sparse representation (SR) techniques. The applications of these techniques have appeared in many different areas ranging from handwritten...
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