Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/43662
Title: | Nonnegative matrix factorization with manifold regularization and maximum discriminant information | Authors: | Hu, W Choi, KS Tao, J Jiang, Y Wang, S |
Issue Date: | Oct-2015 | Source: | International journal of machine learning and cybernetics, Oct. 2015, v. 6, no. 5, p. 837-846 | Abstract: | Nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product of two matrices (respectively denoted as the basis vectors and the encoding vectors), whose entries are constrained to be nonnegative. Unlike the ordinary NMF, we propose a novel NMF, denoted as MMNMF, which considers both geometrical information and discriminative information hidden in the data. The geometrical information is discovered by minimizing the distance among the encoding vectors, while the discriminative information is uncovered by maximizing the distance among base vectors. Clustering experiments are performed on the real-world data sets of faces, images, and documents to demonstrate the effectiveness of the proposed algorithm. | Keywords: | Clustering Manifold regularization Maximum information Nonnegative matrix factorization |
Publisher: | Springer | Journal: | International journal of machine learning and cybernetics | ISSN: | 1868-8071 (print) | DOI: | 10.1007/s13042-015-0396-8 | Rights: | © Springer-Verlag Berlin Heidelberg 2015 This is a post-peer-review, pre-copyedit version of an article published in International Journal of Machine Learning and Cybernetics. The final authenticated version is available online at: http://dx.doi.org/10.1007/s13042-015-0396-8. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
a0597-n09_448.pdf | Pre-Published version | 1.36 MB | Adobe PDF | View/Open |
Page views
118
Last Week
1
1
Last month
Citations as of Apr 28, 2024
Downloads
73
Citations as of Apr 28, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.