Unsupervised Feature Selection with self-Weighted and ℓ_2,0-Norm Constraint

At data mining field, it is a fundamental problem to dispose of high-dimensional data. Many existing unsupervised methods select features by manifold learning or exploring spectral analysis, thus preserving the intrinsic structure of raw data. But most of them follow an assumption that all features are equally importance. To settle this problem, we draw a novel feature selection module that simultaneously performs learning of feature weights matrix, similarity graph structure and projection matrix, so that the local structure after feature weighting and subspace sparse projection is received. Finally, we solve the model based on ℓ_2,0-norm directly by an iterative optimization algorithm and demonstrate the feasibility and effectiveness of our approach via extensive experiments.