Unsupervised Feature Selection via Adaptive Multimeasure Fusion.

Since multiple criteria can be adopted to estimate the similarity among the given data points, problem regarding diverse representations of pairwise relations is brought about. To address this issue, a novel self-adaptive multimeasure (SAMM) fusion problem is proposed, such that different measure functions can be adaptively merged into a unified similarity measure. Different from other approaches, we optimize similarity as a variable instead of presetting it as a priori, such that similarity can be adaptively evaluated based on integrating various measures. To further obtain the associated subspace representation, a graph-based dimensionality reduction problem is incorporated into the proposed SAMM problem, such that the related subspace can be achieved according to the unified similarity. In addition, sparsity-inducing ℓ2,0 regularization is introduced, such that a sparse projection is obtained for efficient feature selection (FS). Consequently, the SAMM-FS method can be summarized correspondingly.