Unsupervised Adaptive Bipartite Graph Embedding

Abstract

In traditional graph embedding methods, graph construction is sensitive to high-dimensional data with noise and outliers, making an effective exploration of the neighborhood structure of the data difficult. Besides, with these methods, constructing graphs and reducing dimensions are disconnected and cannot be mutually optimized. To address these problems, we propose an unsupervised dimensionality reduction method based on bipartite graph, named unsupervised adaptive bipartite graph embedding (UABGE). First, the anchors are generated from the raw data by K-means or random sampling. Second, the bipartite graph, which is constructed between the samples and the anchors in the low-dimensional subspace, utilizes the adaptive allocation method to assign neighbors for each sample, so that the local structure of high-dimensional data can be captured effectively. Third, we present an objective function that combines bipartite graph construction and projection matrix learning to achieve mutual optimization between them, which can be solved with an alternating optimization algorithm. Finally, the computational complexity and the convergence of the algorithm are analyzed. Experimental results on synthetic data and publicly available datasets illustrate the effectiveness of the proposed method.