Diverse Non-Negative Matrix Factorization for Multiview Data Representation
Authors: Wang, J., Tian, F., Yu, H., Liu, C.H., Zhan, K. and Wang, X.
Journal: IEEE Transactions on Cybernetics
Volume: 48
Issue: 9
Pages: 2620-2632
ISSN: 2168-2267
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.
https://eprints.bournemouth.ac.uk/29958/
Source: Scopus
Diverse Non-Negative Matrix Factorization for Multiview Data Representation.
Authors: Wang, J., Tian, F., Yu, H., Liu, C.H., Zhan, K. and Wang, X.
Journal: IEEE Trans Cybern
Volume: 48
Issue: 9
Pages: 2620-2632
eISSN: 2168-2275
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.
https://eprints.bournemouth.ac.uk/29958/
Source: PubMed
Diverse Nonnegative Matrix Factorization for Multi-view Data Representation
Authors: Wang, J., Tian, F., Yu, H.C., liu, C.H.A.N.G.H.O.N.G., Zhan, K. and Wang, X.
Journal: IEEE Transactions on Cybernetics
Publisher: IEEE Advancing Technology for Humanity
ISSN: 2168-2267
https://eprints.bournemouth.ac.uk/29958/
Source: Manual
Diverse Non-Negative Matrix Factorization for Multiview Data Representation.
Authors: Wang, J., Tian, F., Yu, H., Liu, C.H., Zhan, K. and Wang, X.
Journal: IEEE transactions on cybernetics
Volume: 48
Issue: 9
Pages: 2620-2632
eISSN: 2168-2275
ISSN: 2168-2267
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.
https://eprints.bournemouth.ac.uk/29958/
Source: Europe PubMed Central
Diverse Nonnegative Matrix Factorization for Multi-view Data Representation
Authors: Wang, J., Tian, F., Yu, H., Liu, C., Zhan, K. and Wang, X.
Journal: IEEE Transactions on Cybernetics
Volume: 48
Issue: 9
Pages: 2620-2632
ISSN: 2168-2267
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.
https://eprints.bournemouth.ac.uk/29958/
Source: BURO EPrints