In this paper, we address the problem of large-scale multi-view spectral clustering. In many
real-world applications, data can be represented in various heterogeneous features or …

Under the framework of spectral clustering, the key of subspace clustering is building a
similarity graph, which describes the neighborhood relations among data points. Some …

Previous graph-based semisupervised learning (G-SSL) methods have the following
drawbacks: 1) they usually predefine the graph structure and then use it to perform label …

In this review, image clustering problem is discussed starting from global learning based
clustering approaches such as Kmeans to the recent challenges in this domain. In global …

Semi-supervised dimensionality reduction is one of the important topics in pattern
recognition and machine learning. During the past decade, Laplacian Regularized Least …

When no prior knowledge is available, clustering is a useful technique for categorizing data
into meaningful groups or clusters. In this paper, a modified fuzzy min–max (MFMM) …

Spectral clustering is a fundamental technique in the field of data mining and information
processing. Most existing spectral clustering algorithms integrate dimensionality reduction …

Graph construction from data constitutes a pre-stage in many machine learning and
computer vision tasks, like semi-supervised learning, manifold learning, and spectral …

The insufficiency of labeled samples is a major problem in automatic image annotation.
However, unlabeled samples are readily available and abundant. Hence, semi-supervised …

Local learning based image clustering models are usually employed to deal with images
sampled from the non‐linear manifold. Recently, linear discriminant analysis (LDA) based …