19 April 2016 Manifold regularized non-negative matrix factorization with label information
Author Affiliations +
J. of Electronic Imaging, 25(2), 023023 (2016). doi:10.1117/1.JEI.25.2.023023
Non-negative matrix factorization (NMF) as a popular technique for finding parts-based, linear representations of non-negative data has been successfully applied in a wide range of applications, such as feature learning, dictionary learning, and dimensionality reduction. However, both the local manifold regularization of data and the discriminative information of the available label have not been taken into account together in NMF. We propose a new semisupervised matrix decomposition method, called manifold regularized non-negative matrix factorization (MRNMF) with label information, which incorporates the manifold regularization and the label information into the NMF to improve the performance of NMF in clustering tasks. We encode the local geometrical structure of the data space by constructing a nearest neighbor graph and enhance the discriminative ability of different classes by effectively using the label information. Experimental comparisons with the state-of-the-art methods on theCOIL20, PIE, Extended Yale B, and MNIST databases demonstrate the effectiveness of MRNMF.
© 2016 SPIE and IS&T
Huirong Li, Jiangshe Zhang, Changpeng Wang, Junmin Liu, "Manifold regularized non-negative matrix factorization with label information," Journal of Electronic Imaging 25(2), 023023 (19 April 2016). https://doi.org/10.1117/1.JEI.25.2.023023




Machine learning

Ultraviolet radiation

Algorithm development

Principal component analysis


Psychophysical evaluation of document visual similarity
Proceedings of SPIE (February 21 2012)
Efficient mining of strongly correlated item pairs
Proceedings of SPIE (April 18 2006)
Stable factorization of Hankel and Hankel-like matrices
Proceedings of SPIE (November 02 1999)
Face recognition experiments with random projection
Proceedings of SPIE (March 28 2005)

Back to Top