Robust low-rank representation via residual projection for image classification

What is it about?

Noisy interference and high dimensionality are the main challenges in image classification. In this paper, a robust low-rank representation via residual projection is proposed. Different from traditional low-rank representation methods, which regard residues as noisy data or outliers, our proposed method tries to find robust features by learning the structure of residues. That is, we regard residues as a measurement of input data and its low-rank representation. Therefore, robust low-rank projections are then learned from residues to find a good matching structure between input data and its low-rank representation, which leads to accomplishing dimensionality reduction and noise suppression simultaneously. Experimental results on several public image datasets, such as ORL, Yale, YaleB, AR, LFW, COIL20, COIL100, and Caltech101, demonstrate that our proposed method can achieve higher classification accuracy than the state-of-the-art (SOTA) methods in the image classification task. Furthermore, the classification results are much better than those of comparison methods in corrupted datasets, demonstrating that our proposed method can achieve the best robust low-rank representation in noisy datasets.

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Why is it important?

As far as we know, this is the first attempt to learn low-rank projections through the residual structure, unlike the traditional methods, which view residuals as noise.

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