Low-rank representation (LRR) and its variants have been proved to be powerful tools for
handling subspace segmentation problems. In this paper, we propose a new LRR-related …

Low rank representation (LRR) is one of the state-of-the-art methods for subspace clustering
and it has been used widely in machine learning, data mining, and pattern recognition. The …

For the low-rank representation-based subspace clustering, the affinity matrix is block
diagonal. In this paper, a novel robust discriminant low-rank representation (RDLRR) …

Subspace clustering aims to group high-dimensional data samples into several subspaces
which they were generated. Among the existing subspace clustering methods, spectral …

Sparse representation is a powerful tool for subspace clustering, but most existing methods
for this issue ignore the local manifold information in learning procedure. To this end, in this …

With the rapid popularity of multi-camera networks, one human action is usually captured by
multiple cameras located at different angles simultaneously. Multi-camera videos contain the …

Spectral-type subspace clustering algorithms have attracted wide attention because of their
excellent performance displayed in a great deal of applications in machine learning domain …

Low-rank representation (LRR) has shown its great power in subspace segmentation tasks.
However, by using matrix factorization skill, fixed-rank representation dominates LRR in …

Low-rank representation (LRR) and its extensions have shown prominent performances in
subspace segmentation tasks. Among these algorithms, structured-constrained low-rank …

Vehicle detection in high-resolution aerial images has received widespread interests when it
comes to providing the required information for traffic management and urban planning. It is …