Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …

State-of-the-art subspace clustering methods are based on the self-expressive model, which
represents each data point as a linear combination of other data points. However, such …

State-of-the-art subspace clustering methods are based on self-expressive model, which
represents each data point as a linear combination of other data points. By enforcing such …

Subspace clustering methods based on expressing each data point as a linear combination
of a few other data points (eg, sparse subspace clustering) have become a popular tool for …

Subspace clustering aims at discovering the intrinsic structure of data in unsupervised
fashion. As ever in most of approaches, an affinity matrix is constructed by learning from …

Accurate unsupervised classification of hyperspectral images (HSIs) is challenging and has
drawn widespread attention in remote sensing due to its inherent complexity. Although …

Robust principal component analysis (RPCA) can recover low-rank matrices when they are
corrupted by sparse noises. In practice, many matrices are, however, of high rank and …

Self-expressive model is a method for clustering data drawn from a union of low-
dimensional linear subspaces. It gains a lot of popularity due to its: 1) simplicity, based on …

Rank minimization-based subspace clustering methods have been widely developed in the
past decades. Although some smooth surrogates, such as the nuclear norm and Schatten-p …