Multi-target feature selection with subspace learning and manifold regularization.

Existing supervised Multi-Target Feature Selection (MTFS) methods seldom consider the nearest-neighbor relationship and statistical correlation of samples underlying the output space, which leads the result of feature selection to be easily interfered by the output noise, thus making it difficult to achieve satisfactory performance. This paper proposes a novel MTFS method to preserve both the global and local target correlations. Specifically, the low-rank constraint is introduced to achieve multi-layer regression structure to better decouple the inter-input and inter-target relationships. Moreover, the local nearest-neighbor relationships and variable correlations of the sample points in the output space are also explored through adaptive graph and manifold learning, to better utilize the target correlations to improve the MTFS performance. Following the above principle, the resulting objective function and the corresponding optimization algorithm are proposed. Extensive experiments on several public datasets show that the proposed method is superior to other state-of-the-art methods. • A new Embedded MTFS method is proposed to learn the feature redundancy and target correlations in MTR problems. • The nearest-neighbor relationships and target correlations are introduced to achieve superior performance in MTFS tasks. • Extensive experiments on several public datasets show that the proposed method is superior to the compared methods. [ABSTRACT FROM AUTHOR]