Feature Selection via Incorporating Stiefel Manifold in Relaxed K-Means

The task of feature selection is to find the optimal feature subset such that an appropriate criterion is optimized. It can be seen as a special subspace learning task, where the projection matrix is constrained to be selection matrix. In this paper, a novel unsupervised graph embedded feature selection (GEFS) method is derived from the perspective of incorporating the projected k-means with Stiefel manifold regularization. To achieve more statistical and structural properties, we directly embed unsupervised feature selection algorithm into a clustering algorithm via sparse learning to suppress the projected matrix to be row sparse. Comparative experiments demonstrate the effectiveness of our proposed algorithm in comparison with the traditional methods for feature selection.