arXiv 1707.011662017-07cs.LGcs.AI

Unsupervised Submodular Rank Aggregation on Score-based Permutations

University of Washington（华盛顿大学）；The University of Tokyo（东京大学）；University San Pablo CEU（圣巴布罗CEU大学）

Jun Qi, Xu Liu, Javier Tejedor, Shunsuke Kamijo

英文摘要：

Unsupervised rank aggregation on score-based permutations, which is widely used in many applications, has not been deeply explored yet. This work studies the use of submodular optimization for rank aggregation on score-based permutations in an unsupervised way. Specifically, we propose an unsupervised approach based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave objective functions. In addition, stochastic optimization methods are applied in the training process and efficient algorithms for inference can be guaranteed. The experimental results from Information Retrieval, Combining Distributed Neural Networks, Influencers in Social Networks, and Distributed Automatic Speech Recognition tasks demonstrate the effectiveness of the proposed methods.

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