Diverse Nonnegative Matrix Factorization for Multi-view Data Representation.

Wang, J., Tian, F., Yu, H., Liu, C., Zhan, K. and Wang, X., 2017. Diverse Nonnegative Matrix Factorization for Multi-view Data Representation. IEEE Transactions on Cybernetics.

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DOI: 10.1109/TCYB.2017.2747400

Abstract

Non-negative matrix factorization (NMF), a method for finding parts-based representation of non-negative data, has shown remarkable competitiveness in data analysis. Given that real-world datasets are often comprised of multiple features or views which describe data from various perspectives, it is important to exploit diversity from multiple views for comprehensive and accurate data representations. Moreover, real-world datasets often come with high-dimensional features, which demands the efficiency of low-dimensional representation learning approaches. To address these needs, we propose a diverse NMF (DiNMF) approach. It enhances the diversity, reduces the redundancy among multiview representations with a novel defined diversity term and enables the learning process in linear execution time. We further propose a locality preserved DiNMF (LP-DiNMF) for more accurate learning, which ensures diversity from multiple views while preserving the local geometry structure of data in each view. Efficient iterative updating algorithms are derived for both DiNMF and LP-DiNMF, along with proofs of convergence. Experiments on synthetic and real-world datasets have demonstrated the efficiency and accuracy of the proposed methods against the state-of-the-art approaches, proving the advantages of incorporating the proposed diversity term into NMF.

Item Type:Article
ISSN:2168-2267
Additional Information:© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Uncontrolled Keywords:Diversity representation; multiview learning; non-negative matrix factorization (NMF)
Group:Faculty of Science & Technology
ID Code:29958
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:24 Nov 2017 17:02
Last Modified:24 Nov 2017 17:02

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