Wang, Jing, Tian, Feng, Liu, Weiwei, Wang, Xiao, Zhang, Wenjie and Yamanishi, Kenji (2018) Ranking preserving nonnegative matrix factorization. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp. 2776-2782. ISBN 978-0999241127 (doi:https://doi.org/10.24963/ijcai.2018/385)

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Abstract

Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied. In reality, ordinal relations often exist among data, such as data i is more related to j than to q. Such relative order is naturally available, and more importantly, it truly reflects the latent data structure. Preserving the ordinal relations enables us to find structured representations of data that are faithful to the relative order, so that the learned representations become more discriminative. However, current NMFs pay no attention to this. In this paper, we make the first attempt towards incorporating the ordinal relations and propose a novel ranking preserving nonnegative matrix factorization (RPNMF) approach, which enforces the learned representations to be ranked according to the relations. We derive iterative updating rules to solve RPNMF's objective function with convergence guaranteed. Experimental results with several datasets for clustering and classification have demonstrated that RPNMF achieves greater performance against the state-of-the-arts, not only in terms of accuracy, but also interpretation of orderly data structure.

Item Type:	Conference Proceedings
Title of Proceedings:	Proceedings of the 27th International Joint Conference on Artificial Intelligence
Uncontrolled Keywords:	nonnegative matrix factorization, ranking preserving, semi-supervised learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Mar 2022 13:06
URI:	http://gala.gre.ac.uk/id/eprint/30490

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