Incomplete multiview clustering is a challenging problem in the domain of unsupervised
learning. However, the existing incomplete multiview clustering methods only consider the …

In this article, we propose a novel feature selection approach via explicitly addressing the
long-standing subspace sparsity issue. Leveraging-norm regularization for feature selection …

We propose a novel graph Laplacian-guided coupled tensor decomposition (gLGCTD)
model for fusion of hyperspectral image (HSI) and multispectral image (MSI) for spatial and …

Feature selection can alleviate the problem of the curse of dimensionality by selecting more
discriminative features, which plays an important role in multi-label learning. Recently …

We propose a novel discriminative feature learning method via Max-Min Ratio Analysis
(MMRA) for exclusively dealing with the long-standing “worst-case class separation” …

Dimensionality reduction plays a significant role in high-dimensional data processing, and
Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction …

As one of the most popular supervised dimensionality reduction methods, linear discriminant
analysis (LDA) has been widely studied in machine learning community and applied to …

Semi-supervised learning as one of most attractive problems in machine learning research
field has aroused broad attentions in recent years. In this paper, we propose a novel locality …

Abstract In recent times, Graph Convolution Networks (GCN) have been proposed as a
powerful tool for graph-based semi-supervised learning. In this paper, we introduce a model …

The rapid emergence of high-dimensional data in various areas has brought new
challenges to current ensemble clustering research. To deal with the curse of …