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

Spectral clustering (SC) has been proven to be effective in various applications. However,
the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed …

In multimedia annotation, due to the time constraints and the tediousness of manual tagging,
it is quite common to utilize both tagged and untagged data to improve the performance of …

Graph-based clustering methods have achieved remarkable performance by partitioning the
data samples into disjoint groups with the similarity graph that characterizes the sample …

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 …

In the literature, most existing graph-based semi-supervised learning methods only use the
label information of observed samples in the label propagation stage, while ignoring such …

Unsupervised feature selection plays a significant role in data classification and clustering.
General regression models cannot directly exploit the information on the feature space and …

While sparse coding-based clustering methods have shown to be successful, their
bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep …

Graph-based clustering has shown promising performance in many tasks. A key step of
graph-based approach is the similarity graph construction. In general, learning graph in …

In multimedia annotation, due to the time constraints and the tediousness of manual tagging,
it is quite common to utilize both tagged and untagged data to improve the performance of …