Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Dijun Luo, Heng Huang, Feiping Nie, Chris Ding


In many graph-based machine learning and data mining approaches, the quality of the graph is critical. However, in real-world applications, especially in semi-supervised learning and unsupervised learning, the evaluation of the quality of a graph is often expensive and sometimes even impossible, due the cost or the unavailability of ground truth. In this paper, we proposed a robust approach with convex optimization to ``forge'' a graph: with an input of a graph, to learn a graph with higher quality. Our major concern is that an ideal graph shall satisfy all the following constraints: non-negative, symmetric, low rank, and positive semidefinite. We develop a graph learning algorithm by solving a convex optimization problem and further develop an efficient optimization to obtain global optimal solutions with theoretical guarantees. With only one non-sensitive parameter, our method is shown by experimental results to be robust and achieve higher accuracy in semi-supervised learning and clustering under various settings. As a preprocessing of graphs, our method has a wide range of potential applications machine learning and data mining.