Double sparse-representation feature selection algorithm for classification

since amount of unlabeled and high-dimensional datasets need to be preprocessed, unsupervised learning plays a more and more important role in machine learning field. This paper proposed a novel unsupervised feature selection algorithm that can select informative features from dataset without label, by mixing two sparse representation and self-representation loss function into a unified framework. That is, we use self-representation loss function to represent every feature with remainder features and achieve minimum reconstruction mirror, and then utilize l2 , 1-norm regularization term and l1-norm regularization term simultaneously to enforce coefficient matrix to be sparse, such that filter redundant and irrelative features in order to conduct feature selection, where l2 , 1-norm regularization can enforce group sparsity while l1-norm regularization enforce element sparsity. By this way that utilize both of sparse representation terms, we can choose representative features more accurately. At final, we feed reduced data into support vector machine (SVM) to conduct classification accuracy, which is main assessment criteria to validate performance of algorithm. Extensive experiments on synthetic datasets and real-world datasets have exhibited that our proposed method outperform most of common-used methods, such as PCA, LPP and so on.