Subspace Learning for Feature Selection via Rank Revealing QR Factorization: Unsupervised and Hybrid Approaches with Non-negative Matrix Factorization and Evolutionary Algorithm

TL;DR

This paper introduces a novel unsupervised and hybrid feature selection method using rank revealing QR factorization combined with non-negative matrix factorization and genetic algorithms, demonstrating robustness across various datasets and classifiers.

Contribution

It proposes a new feature selection technique leveraging RRQR for efficiency and combines it with NMF and genetic algorithms for improved performance.

Findings

The method is computationally cheaper than SVD-based techniques.

It outperforms or matches state-of-the-art algorithms on microarray datasets.

The hybrid approach effectively removes redundant features and selects discriminative ones.

Abstract

The selection of most informative and discriminative features from high-dimensional data has been noticed as an important topic in machine learning and data engineering. Using matrix factorization-based techniques such as nonnegative matrix factorization for feature selection has emerged as a hot topic in feature selection. The main goal of feature selection using matrix factorization is to extract a subspace which approximates the original space but in a lower dimension. In this study, rank revealing QR (RRQR) factorization, which is computationally cheaper than singular value decomposition (SVD), is leveraged in obtaining the most informative features as a novel unsupervised feature selection technique. This technique uses the permutation matrix of QR for feature selection which is a unique property to this factorization method. Moreover, QR factorization is embedded into non-negative matrix factorization (NMF) objective function as a new unsupervised feature selection method. Lastly, a hybrid feature selection algorithm is proposed by coupling RRQR, as a filter-based technique, and a Genetic algorithm as a wrapper-based technique. In this method, redundant features are removed using RRQR factorization and the most discriminative subset of features are selected using the Genetic algorithm. The proposed algorithm shows to be dependable and robust when compared against state-of-the-art feature selection algorithms in supervised, unsupervised, and semi-supervised settings. All methods are tested on seven available microarray datasets using KNN, SVM and C4.5 classifiers. In terms of evaluation metrics, the experimental results shows that the proposed method is comparable with the state-of-the-art feature selection.