Unsupervised feature selection by regularized matrix factorization

Feature selection is an interesting and challenging task in data analysis process. In this paper, a novel algorithm named Regularized Matrix Factorization Feature Selection (RMFFS) is proposed for unsupervised feature selection. Compared with other matrix factorization based feature selection methods, a main advantage of our algorithm is that it takes the correlation among features into consideration. Through introducing an inner product regularization into our algorithm, the features selected by RMFFS would not only well represent the original high-dimensional data, but also contain low redundancy. Moreover, a simple yet efficient iteratively updating algorithm is also developed to solve the proposed RMFFS. Extensive experimental results on nine real world databases demonstrate that our proposed method can achieve better performance than some state-of-the-art unsupervised feature selection methods.