Abstract
This paper investigated a feature learning problem, where localized and part-based representations are embedded in dimension-reduction progress. We propose a subspace learning model based on nonnegative matrix factorization (NMF) framework. The proposed NMF model incorporates wisely the spatial location information constraint to the base matrix, the manifold and max-margin penalty to the projective feature matrix for enhancing the capability of exploiting much information of the original data. Experiments on facial expression recognition scenery reveal powerful data representation ability of the proposed NMF method that enables dictionary learning and feature learning jointly.
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Duong, VH., Bui, MQ., Wang, JC. (2021). Nonnegative Feature Learning by Regularized Nonnegative Matrix Factorization. In: Kumar, R., Quang, N.H., Kumar Solanki, V., Cardona, M., Pattnaik, P.K. (eds) Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-7527-3_5
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DOI: https://doi.org/10.1007/978-981-15-7527-3_5
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