期刊
SIGNAL PROCESSING-IMAGE COMMUNICATION
卷 62, 期 -, 页码 139-148出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.image.2018.01.001
关键词
Non-negative matrix factorization (NMF); Orthogonal property; Label consistence; Image classification
资金
- National Key Research and Development Program of China [2017YFB0304200]
- National Natural Science Foundation of China [51374063]
- Fundamental Research Funds for the Central Universities [N141008001, N150308001]
As one of the most popular data-representation methods, non-negative matrix factorization (NMF) has been widely used in image processing and pattern recognition. Compared with other dimension reduction methods, we can interpret the data with psychological intuition using NMF since NMF can decompose the whole into visual parts by learning the non-negative basis. However, the original NMF lacks of extracting the discriminant information of the data for the image classification task. For enhancing the discriminant and parts-based interpretability, this work proposes a label and orthogonality regularized NMF (LONMF) algorithm based on the squared Euclidean distance. LONMF takes into account the label consistence with the low-dimensional projected data and orthogonal property of the non-negative basis. By integrating the non-negative constraint, label consistence, and orthogonal property into the objective function, the efficient updating procedure can obtain a discriminant basis matrix. Meanwhile, we design a linear classifier using the projected data to guide the label for efficient image classification task. Experiment results of the competitive NMF variants on the challenging digit and face databases demonstrate the effectiveness of the proposed LONMF algorithm. (C) 2018 Elsevier B.V. All rights reserved.
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