Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

While many existing graph neural networks (GNNs) have been proven to perform $\ell_2 $-
based graph smoothing that enforces smoothness globally, in this work we aim to further …

Existing subspace clustering methods typically employ shallow models to estimate
underlying subspaces of unlabeled data points and cluster them into corresponding groups …

Most popular clustering methods map raw image data into a projection space in which the
clustering assignment is obtained with the vanilla k-means approach. In this article, we …

Subspace clustering aims to cluster unlabeled samples into multiple groups by implicitly
seeking a subspace to fit each group. Most of existing methods are based on a shallow …

Multi-view clustering (MVC) is a popular technique for improving clustering performance
using various data sources. However, existing methods primarily focus on acquiring …

In this paper, we examine the main advances registered in the last ten years in Pattern
Recognition methodologies based on graph matching and related techniques, analyzing …

Most existing person re-identification (Re-ID) methods are based on supervised learning of
a discriminative distance metric. They thus require a large amount of labelled training image …

Spectral clustering has been a popular data clustering algorithm. This category of
approaches often resort to other clustering methods, such as K-Means, to get the final …

Recently, nuclear norm-based low rank representation (LRR) methods have been popular in
several applications, such as subspace segmentation. However, there exist two limitations …