Abstract
Non-negative matrix factorization (NMF) has been widely used in image processing and pattern recognition fields. Unfortunately, NMF does not consider the geometrical structure and the discriminative information of data, which might make it unsuitable for classification tasks. In addition, NMF only calculates the coefficient matrix of the training data and how to yields the coefficient vector of a new test data is still obscure. In this paper, we propose a novel graph embedding regularized projective non-negative matrix factorization (GEPNMF) method to address the aforementioned problems. By introducing a graph embedding regularization term, the learned subspace can preserve the local geometrical structure of data while maximizing the margins of different classes. We deduce a multiplicative update rule (MUR) to iteratively solve the objective function of GEPNMF and prove its convergence in theory. Experimental results on ORL and CMU PIE databases suggest the effectiveness of GEPNMF.
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Du, H., Hu, Q., Zhang, X., Hou, Y. (2014). Image Feature Extraction via Graph Embedding Regularized Projective Non-negative Matrix Factorization. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_20
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DOI: https://doi.org/10.1007/978-3-662-45646-0_20
Publisher Name: Springer, Berlin, Heidelberg
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