Discriminative Label Relaxed Regression with Adaptive Graph Learning

Abstract

The traditional label relaxation regression (LRR) algorithm directly fits the original data without considering the local structure information of the data. While the label relaxation regression algorithm of graph regularization takes into account the local geometric information, the performance of the algorithm depends largely on the construction of graph. However, the traditional graph structures have two defects. First of all, it is largely influenced by the parameter values. Second, it relies on the original data when constructing the weight matrix, which usually contains a lot of noise. This makes the constructed graph to be often not optimal, which affects the subsequent work. Therefore, a discriminative label relaxation regression algorithm based on adaptive graph (DLRR_AG) is proposed for feature extraction. DLRR_AG combines manifold learning with label relaxation regression by constructing adaptive weight graph, which can well overcome the problem of label overfitting. Based on a large number of experiments, it can be proved that the proposed method is effective and feasible.