Multi-view clustering based on feature selection and semi-non-negative anchor graph factorization.

Multi-view clustering has garnered significant attention due to its capacity to utilize information from multiple perspectives. The concept of anchor graph-based techniques was introduced to manage large-scale data better. However, current methods rely on K-means or uniform sampling to select anchors in the original space.

Contrastive self-representation learning for data clustering.

This paper is concerned with self-representation subspace learning. It is one of the most representative subspace techniques, which has attracted considerable attention for clustering due to its good performance. Among these methods, low-rank representation (LRR) has achieved impressive results for subspace clustering.

Joint feature selection and optimal bipartite graph learning for subspace clustering.

Recently, there has been tremendous interest in developing graph-based subspace clustering in high-dimensional data, which does not require a priori knowledge of the number of dimensions and subspaces. The general steps of such algorithms are dictionary representation and spectral clustering. Traditional methods use the dataset itself as a dictionary when performing dictionary representation.