Dual-dual subspace learning with low-rank consideration for feature selection
The performance of machine learning algorithms can be affected by redundant features of
high-dimensional data. Furthermore, these irrelevant features increase the time of …
high-dimensional data. Furthermore, these irrelevant features increase the time of …
Subspace learning for feature selection via rank revealing QR factorization: Fast feature selection
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 …
gained significant attention in the field of machine learning. Recently, there has been …
Subspace learning using low-rank latent representation learning and perturbation theorem: Unsupervised gene selection
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 …
of machine learning and computational biology. Typically, microarray gene datasets exhibit …
Locality-constrained double-layer structure scaled simplex multi-view subspace clustering
Multi-view subspace clustering has attracted extensive attention in recent years due to the
fact that it can utilize the self-expressive property to reveal the low-dimensional subspace …
fact that it can utilize the self-expressive property to reveal the low-dimensional subspace …
Regularization Functions in Subspace Learning-based Feature Selection: Tutorial
A Moslemi - 2024 - hal.science
This is a tutorial about regularization functions for feature selection using subspace learning.
In this tutorial, sparse regularization, structure learning regularization, rank minimization …
In this tutorial, sparse regularization, structure learning regularization, rank minimization …