Semi-supervised sparse feature selection based on low-dimensional space Hessian regularization considering feature manifolds

Semi-supervised sparse feature selection methods based on graph learning has attracted the attention of related researchers because of its characteristics of selecting highly discriminative features, but most methods proposed in recent years apply the graph Laplacian without speculative capabilities, and do not consider feature manifolds. To solve this problem, this paper proposes a Hessian semi-supervised sparse feature selection algorithm in low dimensional space considering feature manifolds (HSLF). In this method, Hessian regularization was embedded to retains the local manifold structure better; and a Laplacian graph is constructed from feature perspective, so that the feature selection matrix is smooth for the feature manifold structure. Then, an efficient iterative method was proposed to solve the proposed objective function. Finally, the effectiveness of the proposed algorithm is verified by comparison with other related algorithms.