1 research outputs found
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, Combining Distributed Neural Networks,
Influencers in Social Networks, and Distributed Automatic Speech Recognition
tasks demonstrate the effectiveness of the proposed methods