Self-supervised symmetric nonnegative matrix factorization
Author(s)
Author(s)
Jia, Y.
Hou, J.
Kwong, S.
Zhang, Q.
Date Issued
2022
Publisher
IEEE
Journal
IEEE Transactions on Circuits and Systems for Video Technology
Volume
32
Issue
7
Start page
4526
End page
4537
Abstract
Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimization problem, making it sensitive to the initialization of variables. Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S 3 NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information. Specifically, we first perform SNMF repeatedly with a random positive matrix for initialization each time, leading to multiple decomposed matrices. Then, we rank the quality of the resulting matrices with adaptively learned weights, from which a new similarity matrix that is expected to be more discriminative is reconstructed for SNMF again. These two steps are iterated until the stopping criterion/maximum number of iterations is achieved. We mathematically formulate S 3 NMF as a constrained optimization problem, and provide an alternative optimization algorithm to solve it with the theoretical convergence guaranteed. Extensive experimental results on 10 commonly used benchmark datasets demonstrate the significant advantage of our S 3 NMF over 14 state-of-the-art methods in terms of 5 quantitative metrics.
SFU Affiliated Publication
Yes
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