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

This paper studies the subspace clustering problem. Given some data points approximately
drawn from a union of subspaces, the goal is to group these data points into their underlying …

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 …

This paper studies the subspace segmentation problem which aims to segment data drawn
from a union of multiple linear subspaces. Recent works by using sparse representation, low …

Subspace clustering is a powerful technology for clustering data according to the underlying
subspaces. Representation based methods are the most popular subspace clustering …

Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State of the art approaches for solving this problem follow a two-stage approach …

The subspace segmentation problem is addressed in this paper by effectively constructing
an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily …

An improved sparse subspace clustering (ISSC) method is proposed to select an
appropriate band subset for hyperspectral imagery (HSI) classification. The ISSC assumes …

This paper studies the subspace segmentation problem. Given a set of data points drawn
from a union of subspaces, the goal is to partition them into their underlying subspaces they …

Various subspace clustering methods have been successively developed to process multi-
view datasets. Most of the existing methods try to obtain a consensus structure coefficient …