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Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation

2019-11-11 | |  60 |   35 |   0
Abstract We propose a multi-prototype-based algorithm for online learning of soft pairwise-preferences over labels. The algorithm learns soft label preferences via minimization of the proposed soft rank-loss measure, and can learn from total orders as well as from various types of partial orders. The soft pairwise preference algorithm outputs are further aggregated to produce a total label ranking prediction using a novel aggregation algorithm that outperforms existing aggregation solutions. Experiments on synthetic and real-world data demonstrate stateof-the-art performance of the proposed model.

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