Multi-Component Nonnegative Matrix Factorization.

Wang, J., Tian, F., Wang, X., Yu, H.C., Liu, C. and Yang, L., 2017. Multi-Component Nonnegative Matrix Factorization. In: International Joint Conference on Artificial Intelligence, 19-25 August 2017, Melbourne, Australia.

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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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17).Edited by Carles Sierra, IIIA-CSIC Sponsored by International Joint Conferences on Artifical Intelligence (IJCAI)
Group:Faculty of Science & Technology
ID Code:29944
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:13 Nov 2017 11:47
Last Modified:13 Nov 2017 11:47

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