Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Clustering, as an unsupervised learning, is aimed at discovering the natural groupings of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …

The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

Graph-based approaches have been successful in unsupervised and semi-supervised
learning. In this paper, we focus on the real-world applications where the same instance can …

The past few years have witnessed a growth in size and computational requirements for
training and inference with neural networks. Currently, a common approach to address …

In real-world applications, it is often that the collected multi-view data are incomplete, ie,
some views of samples are absent. Existing clustering methods for incomplete multi-view …

Graph-based clustering methods perform clustering on a fixed input data graph. If this initial
construction is of low quality then the resulting clustering may also be of low quality …

Many clustering methods partition the data groups based on the input data similarity matrix.
Thus, the clustering results highly depend on the data similarity learning. Because the …

The classification problem is closely related to the clustering problem discussed in Chaps. 6
and 7. While the clustering problem is that of determining similar groups of data points, the …

In this paper, we propose a general framework for incomplete multiview clustering. The
proposed method is the first work that exploits the graph learning and spectral clustering …