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Towards a unified multi-source-based optimization framework for multi-label learning
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DOI：10.1016/j.asoc.2018.12.016.png)
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Abstract
En 中文
In the era of Big Data, a practical yet challenging task is to make learning techniques more universally applicable in dealing with the complex learning problem, such as multi-source multi-label learning. While some of the early work have developed many effective solutions for multi-label classification and multi-source fusion separately, in this paper we learn the two problems together, and propose a novel method for the joint learning of multiple class labels and data sources, in which an optimization framework is constructed to formulate the learning problem, and the result of multi-label classification is induced by the weighted combination of the decisions from multiple sources. The proposed method is responsive in exploiting the label correlations and fusing multi-source data, especially in the fusion of long-tail data. Experiments on various multi-source multi-label data sets reveal the advantages of the proposed method. (C) 2018 Published by Elsevier B.V.
Keywords:
Multi-label learning
Multi-source fusion
Optimization framework
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