Multi-component nonnegative matrix factorization
Authors: Wang, J., Tian, F., Wang, X., Yu, H., Liu, C.H. and Yang, L.
Journal: IJCAI International Joint Conference on Artificial Intelligence
Volume: 0
Pages: 2922-2928
ISBN: 9780999241103
ISSN: 1045-0823
DOI: 10.24963/ijcai.2017/407
Abstract:Real data are usually complex and contain various components. For example, face images have expressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. Therefore, exploring the semantic information of multiple components as well as the diversity among them is of great benefit to understand data comprehensively and in-depth. However, this cannot be achieved by current nonneg-ative matrix factorization (NMF)-based methods, despite that NMF has shown remarkable competitiveness in learning parts-based representation of data. To overcome this limitation, we propose a novel multi-component nonnegative matrix factorization (MCNMF). Instead of seeking for only one representation of data, MCNMF learns multiple representations simultaneously, with the help of the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term. HSIC explores the diverse information among the representations, where each representation corresponds to a component. By integrating the multiple representations, a more comprehensive representation is then established. Extensive experimental results on real-world datasets have shown that MCNMF not only achieves more accurate performance over the state-of-the-arts using the aggregated representation, but also interprets data from different aspects with the multiple representations, which is beyond what current NMFs can offer.
https://eprints.bournemouth.ac.uk/29944/
Source: Scopus
Multi-Component Nonnegative Matrix Factorization
Authors: Wang, J., Tian, F., Wang, X., Yu, H., Liu, C.H. and Yang, L.
Journal: PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
Pages: 2922-2928
https://eprints.bournemouth.ac.uk/29944/
Source: Web of Science (Lite)
Multi-Component Nonnegative Matrix Factorization
Authors: Wang, J., Tian, F., Wang, X., Yu, H.C., Liu, C.H. and Yang, L.
Conference: International Joint Conference on Artificial Intelligence
Dates: 19-25 August 2017
https://eprints.bournemouth.ac.uk/29944/
Source: Manual
Multi-Component Nonnegative Matrix Factorization
Authors: Wang, J., Tian, F., Wang, X., Yu, H.C., Liu, C. and Yang, L.
Conference: International Joint Conference on Artificial Intelligence
Publisher: International Joint Conferences on Artificial Intelligence
ISBN: 978-0-9992411-0-3
Abstract:Real data are usually complex and contain various components. For example, face images have ex- pressions and genders. Each component mainly re- flects one aspect of data and provides information others do not have. Therefore, exploring the seman- tic information of multiple components as well as the diversity among them is of great benefit to un- derstand data comprehensively and in-depth. How- ever, this cannot be achieved by current nonneg- ative matrix factorization (NMF)-based methods, despite that NMF has shown remarkable compet- itiveness in learning parts-based representation of data. To overcome this limitation, we propose a novel multi-component nonnegative matrix factor- ization (MCNMF). Instead of seeking for only one representation of data, MCNMF learns multiple representations simultaneously, with the help of the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term. HSIC explores the diverse infor- mation among the representations, where each rep- resentation corresponds to a component. By inte- grating the multiple representations, a more com- prehensive representation is then established. Ex- tensive experimental results on real-world datasets have shown that MCNMF not only achieves more accurate performance over the state-of-the-arts us- ing the aggregated representation, but also inter- prets data from different aspects with the multi- ple representations, which is beyond what current NMFs can offer.
https://eprints.bournemouth.ac.uk/29944/
Source: BURO EPrints