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Poster
Zeta Hull Pursuits: Learning Nonconvex Data Hulls
Yuanjun Xiong · Wei Liu · Deli Zhao · Xiaoou Tang

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D #None
in Poster Session and Reception »

Selecting a small informative subset from a given dataset, also called column sampling, has drawn much attention in machine learning. For incorporating structured data information into column sampling, research efforts were devoted to the cases where data points are fitted with clusters, simplices, or general convex hulls. This paper aims to study nonconvex hull learning which has rarely been investigated in the literature. In order to learn data-adaptive nonconvex hulls, a novel approach is proposed based on a graph-theoretic measure that leverages graph cycles to characterize the structural complexities of input data points. Employing this measure, we present a greedy algorithmic framework, dubbed Zeta Hulls, to perform structured column sampling. The process of pursuing a Zeta hull involves the computation of matrix inverse. To accelerate the matrix inversion computation and reduce its space complexity as well, we exploit a low-rank approximation to the graph adjacency matrix by using an efficient anchor graph technique. Extensive experimental results show that data representation learned by Zeta Hulls can achieve state-of-the-art accuracy in text and image classification tasks.

Author Information

Yuanjun Xiong (The Chinese University of Hong Kong)
Wei Liu (Tencent AI Lab)
Deli Zhao (Xiaomi AI Lab)
Xiaoou Tang (Chinese University of Hong Kong)

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