Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149879
Title: Robust nonnegative matrix factorization with local coordinate constraint for image clustering
Authors: Peng, Siyuan
Ser, Wee
Chen, Badong
Sun, Lei
Lin, Zhiping
Keywords: Engineering::Computer science and engineering::Computing methodologies
Issue Date: 2020
Source: Peng, S., Ser, W., Chen, B., Sun, L. & Lin, Z. (2020). Robust nonnegative matrix factorization with local coordinate constraint for image clustering. Engineering Applications of Artificial Intelligence, 88, 103354-. https://dx.doi.org/10.1016/j.engappai.2019.103354
Journal: Engineering Applications of Artificial Intelligence
Abstract: Nonnegative matrix factorization (NMF) has attracted increasing attention in data mining and machine learning. However, existing NMF methods have some limitations. For example, some NMF methods seriously suffer from noisy data contaminated by outliers, or fail to preserve the geometric information of the data and guarantee the sparse parts-based representation. To overcome these issues, in this paper, a robust and sparse NMF method, called correntropy based dual graph regularized nonnegative matrix factorization with local coordinate constraint (LCDNMF) is proposed. Specifically, LCDNMF incorporates the geometrical information of both the data manifold and the feature manifold, and the local coordinate constraint into the correntropy based objective function. The half-quadratic optimization technique is utilized to solve the nonconvex optimization problem of LCDNMF, and the multiplicative update rules are obtained. Furthermore, some properties of LCDNMF including the convergence, relation with gradient descent method, robustness, and computational complexity are analyzed. Experiments of clustering demonstrate the effectiveness and robustness of the proposed LCDNMF method in comparison to several state-of-the-art methods on six real world image datasets.
URI: https://hdl.handle.net/10356/149879
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2019.103354
Rights: © 2019 Elsevier Ltd. All rights reserved. This paper was published in Engineering Applications of Artificial Intelligence and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20220228
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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