The increasing access to health data worldwide is driving a resurgence in machine learning
research, including data-hungry deep learning algorithms. More computationally efficient …

The feature selection problem is one of the pre-processing mechanisms to find the optimal
subset of features from a dataset. The search space of the problem will exponentially grow …

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells
(iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells …

We consider situations in which the clustering of some multivariate data is desired, which
establishes an ordering of the clusters with respect to an underlying latent variable. As our …

Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation
applications. Manual labeling-based clustering is both time-consuming and less accurate …

Unsupervised feature selection (UFS) has gained increasing attention and research interest
in various domains, such as machine learning and data mining. Recently, numerous matrix …

In this paper, we consider feature screening for ultrahigh dimensional clustering analyses.
Based on the observation that the marginal distribution of any given feature is a mixture of its …

Abstract Model-based clustering is a commonly-used technique to partition heterogeneous
data into homogeneous groups. When the analysis is to be conducted with a large number …

Feature selection plays a crucial role in classification by identifying relevant features. Bio-
inspired algorithms, such as genetic algorithms, particle swarm optimization, and ant colony …

Hierarchical clustering arrange multi-dimensional data into a tree-like structure, organizing
the data by increasing levels of similarity. A cut of the tree divides data into clusters, where …