Journal
SIGNAL PROCESSING-IMAGE COMMUNICATION
Volume 95, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.image.2021.116253
Keywords
Nonnegative matrix factorization; Nonnegative Adaptive graph regularization; Semi-supervised learning
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Funding
- National Natural Science Foundation of China [11961010, 61967004]
- Guangxi Natural Science Foundation [2018GXNSFAA138169]
- Innovation Project of GUET Graduate Education [2020YCXS087]
- Guangxi key laboratory of automatic testing technology and instruments ( [YQ19111]
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This paper introduces AGDNMF, an adaptive graph-based discriminative NMF method that utilizes label information to improve data representation and obtain the neighbor graph through adaptive iterations, which has been proven effective in various image datasets compared to state-of-the-art methods.
Nonnegative matrix factorization(NMF) is an effective dimension reduction method, which is widely used in image clustering and other fields. Some NMF variants preserve the manifold structure of the original data. However, the construction of the traditional neighbor graph depends on the original data, so it may be affected by noise and outliers. Moreover, these methods are unsupervised and do not use available label information. Therefore, this paper presents an adaptive graph-based discriminative nonnegative matrix factorization(AGDNMF). AGDNMF uses the available label to construct the label matrix, such that the new representations with the same label data are aligned to the same axis. And the neighbor graph in AGDNMF is obtained by adaptive iterations. A number of experiments on many image data sets verify that AGDNMF is effective compared with the other state-of-the-art methods.
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