Multi-view clustering divides similar objects into the same class through using the fused
multiview information. Most multi-view clustering methods obtain clustering result by only …

Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …

Feature selection (FS) is one of the most critical tasks in data mining, which aims to reduce
the dimensionality of the data and maximize classification accuracy. The FS problem can be …

Consensus clustering provides an elegant framework to aggregate multiple weak clustering
results to learn a consensus one that is more robust and stable than a single result …

As data with diverse representations become highdimensional, multi-view unsupervised
feature selection has been an important learning paradigm. Generally, existing methods …

In this article, we propose a new unsupervised feature selection method named pseudo-
label guided structural discriminative subspace learning (PSDSL). Unlike the previous …

Feature selection plays an important role in data analysis, yet traditional graph-based
methods often produce suboptimal results. These methods typically follow a two-stage …

Deep clustering has attracted increasingly more attention in recent years. However, due to
the absence of labels, deep clustering sometimes still provides unreliable clustering results …

Feature selection is an indispensable aspect of modern machine learning, especially for
high-dimensional datasets where overfitting and computational inefficiencies are common …

Spectral feature selection, an excellent dimensionality reduction method, is extensively used
in knowledge mining and protein sequence analysis. However, the graph representation …