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Robust nonnegative matrix factorization with ordered structure constraints

Robust nonnegative matrix factorization with ordered structure constraints

Wang, Jing, Tian, Feng, Liu, Chang Hong, Yu, Hongchuan, Wang, Xiao and Tang, Xianchao (2017) Robust nonnegative matrix factorization with ordered structure constraints. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 478-485. ISBN 978-1509061839 ISSN 2161-4407 (Online) (doi:https://doi.org/10.1109/IJCNN.2017.7965892)

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Abstract

Nonnegative matrix factorization (NMF) as a popular technique to find parts-based representations of nonnegative data has been widely used in real-world applications. Often the data which these applications process, such as motion sequences and video clips, are with ordered structure, i.e., consecutive neighbouring data samples are very likely share similar features unless a sudden change occurs. Therefore, traditional NMF assumes the data samples and features to be independently distributed, making it not proper for the analysis of such data. In this paper, we propose an ordered robust NMF (ORNMF) by capturing the embedded ordered structure to improve the accuracy of data representation. With a novel neighbour penalty term, ORNMF enforces the similarity of neighbouring data. ORNMF also adopts the L 2,1 -norm based loss function to improve its robustness against noises and outliers. A new iterative updating optimization algorithm is derived to solve ORNMF's objective function. The proofs of the convergence and correctness of the scheme are also presented. Experiments on both synthetic and real-world datasets have demonstrated the effectiveness of ORNMF.

Item Type: Conference Proceedings
Title of Proceedings: 2017 International Joint Conference on Neural Networks (IJCNN)
Uncontrolled Keywords: nonnegative matrix factorization, ordered structure, unsupervised learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 22 Jan 2021 11:34
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/30499

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