Abstract
PCANet is a deep learning network that uses an orthogonal factorization form (i.e., principal component analysis, PCA) to learn the filter bank. It shows superior performance for image classification task. However, PCA only considers the statistics of the image patches while it does not consider the part-based matrix factorization (i.e. Non-negative Matrix Factorization). In this paper, we proposed to replace the PCA algorithm with the Semi-Non-Negative Matrix Factorization (SNMF) algorithm and to construct the SNMFNet. Specifically, first, we propose a seminon- negative matrix factorization algorithm, which uses the l2 norm regularizer to restrict base vectors to substitute the nonnegative constraint on basis matrix. Second, we introduce the SNMFNet, which uses the nonorthogonal matrix factorization algorithm to replace the orthogonal matrix factorization such as PCANet. Experimental results show that our proposed SNMFNet achieves superior performance to PCANet on several benchmark datasets.
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