Multimedia Laboratory

Projects / Machine Learning


Pattern clustering is a fundamental task in machine learning. It is still challenging to cluster data that is of multiple distributions. We develop robust clustering algorithms to address such issues by means of Zeta function of a graph, maximum incremental path integral, and directed graph degree linkage.

Agglomerative clustering via maximum incremental path integral
W. Zhang, D.L. Zhao and X.O. Tang. Pattern Recognition, 2013
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Graph Degree Linkage: Agglomerative Clustering on a Directed Graph.
W. Zhang, X.G. Wang, D.L. Zhao and X.O. Tang. In Proceedings of European Conference on Computer Vision (Poster, ECCV 2012).
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Cyclizing Clusters via Zeta Function of a Graph.
D.L. Zhao and X.O. Tang. In Proceedings of Neural Information Processing Systems (Poster, NIPS 2008).
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The Chinese University of Hong Kong 香港中文大学

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