With the rapid development of science and technology, high-dimensional data have been
widely used in various fields. Due to the complex characteristics of high-dimensional data, it …

Subspace clustering has attracted much attention in many applications of computer vision
and pattern recognition. Spectral clustering based methods, such as sparse subspace …

A novel online joint kernel learning and clustering (OKC) framework is derived which is
capable of determining time-varying clustering configurations without the need for training …

Many feature extraction methods based on subspace learning have been proposed and
applied with good performance. Most existing methods fail to achieve a balance between …

Unveiling the intrinsic structure within high-dimensional data presents a significant
challenge, particularly when clusters manifest themselves in lower-dimensional subspaces …

Deep image clustering networks have the capability to categorize unlabeled images,
thereby effectively utilizing them. This paper synthesizes recent researches about deep …

Subspace clustering has attracted significant interest for its capacity to partition high-
dimensional data into multiple subspaces. The current approaches to subspace clustering …

Deep subspace clustering methods based on data self-representation have become highly
popular owing to their ability to automatically discover more suitable feature spaces …

Most existing subspace clustering methods preprocess image data by converting them into
vectors, which lacks exploration of the spatial structure of high-dimensional data. Therefore …

As one of the most effective subspace clustering methods, the self-expression based
sparsity method leverages the robust representational learning and non-linear …