Manifold regularized robust unsupervised feature selection for image clustering
Yuqing Shi, Shiqiang Du · 2017
Dimensionality reduction is a challenging task for high dimensional data processing in machine learning and data mining. As an effective dimension reduction technique, unsupervised feature selection aims at finding a subset of features to retain the most relevant information. In this paper, we propose a novel unsupervised feature selection method, called Manifold Regularized Robust Unsupervised Feature Selection (MRUFS) for image clustering. MRUFS performs robust discriminative feature selection and robust clustering simultaneously under ℓ_2,1-norm while preserves the local manifold structures of original data. Compared with several unsupervised feature selection methods, the proposed algorithm comes with better clustering performance for two datasets: FERET and COIL20 which we have experimented with here.