Label Propagation Algorithm Based on Weighted Samples and Consensus-rate

Label Propagation is one of the most widely used semi-supervised classification methods.Consensus rate-based label propagation(CRLP) algorithm constructs the graph by summarizing multiple clustering solutions to incorporate various properties of the data.Like most graph-based semi-supervised classification method,CRLP focuses on optimizing the graph to improve the performance.In fact,samples are not always evenly distributed.The importance of different samples in the algorithm is diffe-rent.CRLP algorithm is easily affected by the numbers of clustering and the clustering methods,and it is not adaptable to low-dimensional data.To deal with these problems,a label propagation algorithm based on weighted samples and consensus-rate(WSCRLP) is proposed.WSCRLP firstly clusters the dataset multiple times to explore the structure of sample and combines the consensus-rate and the local information of the sample to construct a graph.Secondly,different weights are assigned to labeled samples with different distributions.