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 growing interest in employing matrix factorization-based techniques, such as non-negative matrix factorization, for feature selection. The primary objective of feature selection using matrix factorization is to extract a lower-dimensional subspace that captures the essence of the original space. This study introduces a novel unsupervised feature selection technique that leverages rank revealing QR (RRQR) factorization. Compared to singular value decomposition (SVD) and nonnegative matrix factorization (NMF), RRQR is more computationally efficient. The uniqueness of this technique lies in the utilization of the permutation matrix of QR for feature selection. Additionally, we integrate QR factorization into the objective function of NMF to create a new unsupervised feature selection method. Furthermore, we propose a hybrid feature selection algorithm by combining RRQR and a Genetic algorithm. The algorithm eliminates redundant features using RRQR factorization and selects the most distinguishing subset of features using the Genetic algorithm. Experimental comparisons with state-of-the-art feature selection algorithms in supervised, unsupervised, and semi-supervised settings demonstrate the reliability and robustness of the proposed algorithm. The evaluation is conducted on eight microarray datasets using KNN, SVM, and C4.5 classifiers. The experimental results indicate that the proposed method achieves comparable performance to the state-of-the-art feature selection methods. Our empirical findings demonstrate that the proposed method exhibits a significantly lower computational cost compared to other techniques.