An orthogonal representation of features can offer valuable insights into feature selection as
it aims to find a representative subset of features in which all features can be accurately …

Gene selection as a problem with high dimensions has drawn considerable attention in
machine learning and computational biology over the past decade. In the field of gene …

The identification of informative and distinguishing features from high-dimensional data has
gained significant attention in the field of machine learning. Recently, there has been …

The performance of machine learning algorithms can be affected by redundant features of
high-dimensional data. Furthermore, these irrelevant features increase the time of …

In recent years, gene expression data analysis has gained growing significance in the fields
of machine learning and computational biology. Typically, microarray gene datasets exhibit …

Unsupervised feature selection techniques have shown promising results in dealing with
unlabelled high-dimensional data. Laplacian graph-based techniques with l 2, 1-norm row …

The sparse identification of nonlinear dynamical systems (SINDy) is a data-driven technique
employed for uncovering and representing the fundamental dynamics of intricate systems …

The design and control of turbomachinery require a wealth of spatiotemporal data. Thus, the
low-cost and robust estimation of global aerodynamics from extremely limited data and noisy …

Predicting therapy response of Head & Neck (H&N) cancers prior to therapy initiation can be
effective to increase the probability of pathologic complete response (pCR) and clinical …

Partial differential equation parameter estimation is a mathematical and computational
process used to estimate the unknown parameters in a partial differential equation model …