Subspace distance is an invaluable tool exploited in a wide range of feature selection
methods. The power of subspace distance is that it can identify a representative subspace …

Single-cell technologies can measure the expression of thousands of molecular features in
individual cells undergoing dynamic biological processes. While examining cells along a …

Feature selection has been an outstanding strategy in eliminating redundant and inefficient
features in high-dimensional data. This paper introduces a novel unsupervised feature …

Tabular datasets with low-sample-size or many variables are prevalent in biomedicine.
Practitioners in this domain prefer linear or tree-based models over neural networks since …

A new line of research for feature selection based on neural networks has recently emerged.
Despite its superiority to classical methods, it requires many training iterations to converge …

Discovering relevant input features for predicting a target variable is a key scientific
question. However, in many domains, such as medicine and biology, feature selection is …

Clustering is a fundamental learning task widely used as a first step in data analysis. For
example, biologists use cluster assignments to analyze genome sequences, medical …

Canonical Correlation Analysis (CCA) models are powerful for studying the associations
between two sets of variables. The canonically correlated representations, termed\textit …

In this paper, we present a new method for few-sample supervised feature selection (FS).
Our method first learns the manifold of the feature space of each class using kernels …

In many real world problems, features do not act alone but in combination with each other.
For example, in genomics, diseases might not be caused by any single mutation but require …