Nonlinear subspace clustering by functional link neural networks
Long Shi Lei Cao Zhongpu Chen Badong Chen Yu Zhao
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.