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Title: Robust orthogonal nonnegative matrix tri-factorization for data representation
Authors: Peng, Siyuan
Ser, Wee
Chen, Badong
Lin, Zhiping
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Peng, S., Ser, W., Chen, B. & Lin, Z. (2020). Robust orthogonal nonnegative matrix tri-factorization for data representation. Knowledge-Based Systems, 201-202, 106054-.
Journal: Knowledge-Based Systems
Abstract: Nonnegative matrix factorization (NMF) has been a vital data representation technique, and has demonstrated significant potential in the field of machine learning and data mining. Nonnegative matrix tri-factorization (NMTF) is an extension of NMF, and provides more degrees of freedom than NMF. In this paper, we propose the correntropy based orthogonal nonnegative matrix tri-factorization (CNMTF) algorithm, which is robust to noisy data contaminated by non-Gaussian noise and outliers. Different from previous NMF algorithms, CNMTF firstly applies correntropy to NMTF to measure the similarity, and preserves double orthogonality conditions and dual graph regularization. We adopt the half-quadratic technique to solve the optimization problem of CNMTF, and derive the multiplicative update rules. The complexity issue of CNMTF is also presented. Furthermore, the robustness of the proposed algorithm is analyzed, and the relationships between CNMTF and several previous NMF based methods are discussed. Experimental results demonstrate that the proposed CNMTF method has better performance on real world image and text datasets for clustering tasks, compared with several state-of-the-art methods.
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2020.106054
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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