Semi-supervised classification using multiple clusterings

Abstract

Graph determines the performance of graph-based semi-supervised classification. In this paper, we investigate how to construct a graph from multiple clusterings and propose a method called Semi-Supervised Classification using Multiple Clusterings (SSCMC in short). SSCMC firstly projects original samples into different random subspaces and performs clustering on the projected samples. Then, it constructs a graph by setting an edge between two samples if these two samples are clustered in the same cluster for each clustering. Next, it combines these graphs into a composite graph and incorporates the resulting composite graph with a graph-based semi-supervised classifier based on local and global consistency. Our experimental results on two publicly available facial images show that SSCMC not only achieves higher accuracy than other related methods, but also is robust to input parameters.