Machine Learning

Unsupervised Submodular Rank Aggregation on Score-based Permutations

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    Unsupervised Submodular Rank Aggregation on Score-based Permutations

    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, Combin- ing Distributed Neural Networks, Influencers in Social Networks, and Distributed Automatic Speech Recognition tasks demonstrate the effectiveness of the proposed methods.

    Unsupervised Submodular Rank Aggregation on Score-based Permutations
    by Jun Qi, Xu Liu, Javier Tejedor, Shunsuke Kamijo
    https://arxiv.org/pdf/1707.01166v2.pdf

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