Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation

In order to overcome the drawbacks of the ridge regression and label propagation algorithms, we propose a new semi-supervised classification method named semi-supervised ridge regression with adaptive graph-based label propagation (SSRR-AGLP). Firstly, we present a new adaptive graph-learning scheme and integrate it into the procedure of label propagation, in which the locality and sparsity of samples are considered simultaneously. Then, we introduce the ridge regression algorithm into label propagation to solve the “out of sample” problem. As a consequence, the proposed SSSRR-AGLP integrates adaptive graph learning, label propagation and ridge regression into a unified framework. Finally, an effective iterative updating algorithm is designed for solving the algorithm, and the convergence analysis is also provided. Extensive experiments are conducted on five databases. Through comparing the results with some well-known algorithms, the effectiveness and superiority of the proposed algorithm are demonstrated.