Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Spectral clustering is a clustering method based on algebraic graph theory. It has aroused
extensive attention of academia in recent years, due to its solid theoretical foundation, as …

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives
inspiration primarily from deep learning approaches, achieves state-of-the-art performance …

Clustering is among the most fundamental tasks in computer vision and machine learning. In
this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised …

In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …

Graph and subspace clustering methods have become the mainstream of multi-view
clustering due to their promising performance. However,(1) since graph clustering methods …

We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc,
generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By …