Unsupervised feature selection via maximum projection and minimum redundancy

Dimensionality reduction is an important and challenging task in machine learning and data mining. It can facilitate data clustering, classification and information retrieval. As an efficient technique for dimensionality reduction, feature selection is about finding a small feature subset preserving the most relevant information. In this paper, we propose a new criterion, called maximum projection and minimum redundancy feature selection, to address unsupervised learning scenarios. First, the feature selection is formalized with the use of the projection matrices and then characterized equivalently as a matrix factorization problem. Second, an iterative update algorithm and a greedy algorithm are proposed to tackle this problem. Third, kernel techniques are considered and the corresponding algorithm is also put forward. Finally, the proposed algorithms are compared with four state-of-the-art feature selection methods. Experimental results reported for six publicly datasets demonstrate the superiority of the proposed algorithms.