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Fuzzy clustering method with approximate orthogonal regularization
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DOI：10.1016/j.asoc.2023.110829.png)
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Abstract
En 中文
As a widely used unsupervised learning method, clustering model plays an indispensable role in exploring data structures. Spectral analysis is a useful technique for clustering problems. The spectral analysis could yield good results when used in conjunction with fuzzy clustering models. However, the existing methods are limited by factors such as the definition of the similarity graph and predetermined number of clusters. In this paper, we propose a new fuzzy clustering model with the approximate orthogonal regularization. To obtain the relatively stable results, we reconstruct the Laplacian matrix with geometrically-nearest-neighbor similarity measurement. This new Laplacian matrix can represent the properties of datasets. For this constrained optimization problem, by applying the indicator function to simplify it as an unconstrained optimization problem. Then we propose an algorithm based on Alternating Direction Method of Multipliers (ADMM) and theoretically prove its convergence. We make some empirical experiments on both synthetic and real datasets. The experimental results demonstrate the model's effectiveness and showcase the desirable properties of the resulting solutions.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Spectral clustering
Fuzzy clustering
Orthogonal regularization
Similarity measurement
Alternating direction method of multipliers
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
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