Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation.

ABSTRACT: Feature subspace learning plays a significant role in pattern recognition, and many efforts have been made to generate increasingly discriminative learning models. Recently, several discriminative feature learning methods based on a representation model have been proposed, which have not only attracted considerable attention but also achieved success in practical applications. Nevertheless, these methods for constructing the learning model simply depend on the class labels of the training instances and fail to consider the essential subspace structural information hidden in them. In this paper, we propose a robust feature subspace learning approach based on a low-rank representation. In our approach, the low-rank representation coefficients are considered as weights to construct the constr

SUBMITTER: Li A 

PROVIDER: S-EPMC6504107 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature