Skip navigation

Multi-component nonnegative matrix factorization

Multi-component nonnegative matrix factorization

Wang, Jing, Tian, Feng, Wang, Xiao, Yu, Hongchuan, Liu, Chang Hong and Yang, Liang (2017) Multi-component nonnegative matrix factorization. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp. 2922-2928. ISBN 978-0999241103 (doi:https://doi.org/10.24963/ijcai.2017/407)

[img]
Preview
PDF (Author Accepted Manuscript)
30481 WANG_Multi-Component_Nonnegative_Matrix_2017.pdf - Accepted Version

Download (244kB) | Preview

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 nonnegative 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. A new iterative updating optimization scheme is derived to solve the objective function of MCNMF, along with its correctness and convergence guarantees. 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.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Uncontrolled Keywords: multi-component, NMF, clustering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 14 Jan 2021 14:03
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/30481

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics