Nonlinear subspace clustering by functional link neural networks
This work addresses subspace clustering for data analysis, offering an incremental improvement in efficiency and performance over existing methods.
The paper tackles nonlinear subspace clustering by using a functional link neural network to map data into a nonlinear domain and learn a self-representation matrix, achieving high computational efficiency and improved clustering performance with local similarity regularization and a convex combination scheme. Extensive experiments confirm the advancement of the methods, though specific accuracy numbers are not provided.
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.