The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …

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 …

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

The nuclear norm is widely used as a convex surrogate of the rank function in compressive
sensing for low rank matrix recovery with its applications in image recovery and signal …

Subspace clustering methods based on data self-expression have become very popular for
learning from data that lie in a union of low-dimensional linear subspaces. However, the …

Subspace clustering methods based on ell_1, l_2 or nuclear norm regularization have
become very popular due to their simplicity, theoretical guarantees and empirical success …

More often than not, a multimedia data described by multiple features, such as color and
shape features, can be naturally decomposed of multi-views. Since multi-views provide …

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 …

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

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …