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Highlights • A sharing multi-view feature selection method which combines the specificity of views and the common objective is proposed. • To effectively select features from the high dimensional data via sharing strategy and ADMM. • To speed up the calculation by decomposing the large scale optimization problem into small scale sub-problems. • The comparison experiments with several state-of-the-art feature selection methods show its effectiveness. Abstract The matrix-based multi-view feature selection, which can integrate the information of multiple views for selecting representative features, has attracted wide attention in recent years. In this paper, we propose a novel supervised sharing multi-view feature selection method. The proposed method makes all views share a common penalty that regresses samples to their labels. Meanwhile, it adopts the structured sparsity-inducing norm to implement sparsity for each view. The proposed method considers not only the complementary of different views, but also the specificity of each view instead of concatenating all views into high-dimensional vectors. In addition, the proposed model can be decomposed into N small scale subproblems (where N is the number of views) and solved efficiently via Alternating Direction Method of Multipliers (ADMM), especially for high-dimensional large scale data sets. The comparison experiments with several state-of-the-art feature selection methods show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a neurodynamic approach with a recurrent neural network for solving convex optimization problems with general constraint. It is proved that for any initial point, the state of the proposed neural network reaches the constraint set in finite time, and converges to an optimal solution of the convex optimization problem finally. In contrast to the existing related neural networks, the convergence rate of the state of the proposed neural network can be calculated quantitatively via the Łojasiewicz exponent under some mild assumptions. As applications, we estimate explicitly some Łojasiewicz exponents to show the convergence rate of the state of the proposed neural network for solving convex quadratic optimization problems. And some numerical examples are given to demonstrate the effectiveness of the proposed neural network. [ABSTRACT FROM AUTHOR]