Nonlinear subspace clustering by functional link neural networks

• URL: http://arxiv.org/abs/2402.02051v2
• Date: Tue, 27 Aug 2024 05:26:14 GMT
• Title: Nonlinear subspace clustering by functional link neural networks
• Authors: Long Shi, Lei Cao, Zhongpu Chen, Badong Chen, Yu Zhao,
• Abstract summary: Subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. We employ a functional link neural network to transform data samples into a nonlinear domain. We introduce a convex combination subspace clustering scheme, which combines a linear subspace clustering method with the functional link neural network subspace clustering approach.
• Score: 20.972039615938193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nonlinear subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. While this approach demonstrates impressive outcomes, it involves a balance between effectiveness and computational cost. In this study, we employ a functional link neural network to transform data samples into a nonlinear domain. Subsequently, we acquire a self-representation matrix through a learning mechanism that builds upon the mapped samples. As the functional link neural network is a single-layer neural network, our proposed method achieves high computational efficiency while ensuring desirable clustering performance. By incorporating the local similarity regularization to enhance the grouping effect, our proposed method further improves the quality of the clustering results. Additionally, we introduce a convex combination subspace clustering scheme, which combining a linear subspace clustering method with the functional link neural network subspace clustering approach. This combination approach allows for a dynamic balance between linear and nonlinear representations. Extensive experiments confirm the advancement of our methods. The source code will be released on https://lshi91.github.io/ soon.

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