Subspace clustering methods with sparsity prior, such as Sparse Subspace Clustering
(SSC) 1, are effective in partitioning the data that lie in a union of subspaces. Most of those …

This book provides a broader introduction to the theories and applications of sparse coding
techniques in computer vision research. It introduces sparse coding in the context of …

Consider the recent upswing in interest around multi-view clustering procedures. Such
methods aim to boost clustering efficiency by leveraging information from numerous …

Prototype selection aims to remove redundancy and irrelevance from large-scale data by
selecting an informative subset, which makes it possible to see all data from a few …

Many clustering methods highly depend on extracted features. In this paper, we propose a
joint optimization framework in terms of both feature extraction and discriminative clustering …

Sparse graphs built by sparse representation has been demonstrated to be effective in
clustering high-dimensional data. Albeit the compelling empirical performance, the vanilla …

Subspace clustering methods partition the data that lie in or close to a union of subspaces in
accordance with the subspace structure. Such methods with sparsity prior, such as sparse …

Various subspace clustering methods have benefited from introducing a graph
regularisation term in their objective functions. In this work, we identify two critical limitations …

With the rapid accumulation of high dimensional data, dimensionality reduction plays a more
and more important role in practical data processing and learning tasks. This paper studies …

Human action recognition is defined as determining the actions of humans happening in
video sequences. Human action recognition is one of the interesting topics which can play …