Unsupervised Optimized Bipartite Graph Embedding

Abstract

Graph embedding is a widely used method for dimensionality reduction due to its computational effectiveness. The quality of the graph and the efficiency of graph construction will directly affect the performance and the efficiency of the graph embedding methods. However, in the unsupervised graph embedding methods, the graph is not considered as an optimized graph since there is no label that can be used to construct this graph. In addition, the running of traditional graph embedding methods become very time-consuming on large-scale datasets due to the high computational cost in the step of graph construction. Aiming to solve these problems, we propose an unsupervised dimensionality reduction method based on bipartite graph, called Unsupervised Optimized Bipartite Graph Embedding (UOBGE). Representative anchors are firstly identified in the data. Then, we construct the bipartite graph between the projected samples and the projected anchors and the intrinsic graph connecting all the projected sample pairs with equal weights, which keep the local and global geometric structures of the data, respectively. Finally, the bipartite graph and the projection matrix are optimized simultaneously by introducing an alternating optimization procedure. Extensive experiments on several datasets demonstrate that the effectiveness and efficiency of the proposed method.