In the classification learning tasks, it is inevitable to generate noise in the process of acquiring data. Especially, the existence of label noise not only makes the learning model more complex, but also leads to overfitting and the reduction of generalization ability of the classifier. Although some label noise filtering algorithms can solve the above problems to some extent, there are still some limitations such as poor noise recognition ability, unsatisfactory classification effect and low filtering efficiency. Focused on these issues, a local probability sampling method based on label confidence distribution was proposed for label noise filtering. Firstly, the random forest classifiers were used to perform the voting of the labels of samples, so as to obtain the label confidence of each sample. And then the samples were divided into easy and hard to recognize ones according to the values of label confidences. Finally, the samples were filtered by different filtering strategies respectively. Experimental results show that in the situation of existing label noise, the proposed method can maintain high noise recognition ability in most cases, and has obvious advantage on classification generalization performance.

Real-world network nodes contain a large number of attribute information and there is an overlapping characteristic between communities. Aiming at the problems, an overlapping community detection algorithm for attributed networks was proposed. The network topology structure and node attributes were fused to define the intensity degree and interval degree of network nodes, which were designed to describe the characteristics of community-the dense interior connection and the sparse exterior connection respectively. Based on the idea of density peak clustering, the local density centers were selected as community centers. On this basis, an iteration calculating method for the membership of non-central nodes about each community was proposed, and the division of overlapping communities was realized. The simulation experiments were carried out on real datasets. The experimental results show that the proposed algorithm has better performance in community detection than LINK algorithm, COPRA algorithm and DPSCD (Density Peaks-based Clustering Method).

In recent years, Sparse Representation based Classifier (SRC) has become a hot issue which has been great successful in face recognition. However, when the SRC reconstructed test samples, it is possible to use the training samples with large difference from the test samples, meanwhile, SRC tends to lose locality information and thus produces unstable classification results. A Self-Paced Learning Weighted Sparse Representation based Classifier (SPL-WSRC) was proposed. It could effectively eliminate the training samples with large difference from the test samples. In addition, locality information between the samples was considered by weighting to improve the classification accuracy and stability. The experimental results on three classical face databases show that the proposed SPL-WSRC algorithm is better than the original SRC algorithm. The effect is more obvious, especially when the training samples are enough.