| Summary: | Multi-label learning plays a critical role in the areas of data mining, multimedia, and machine learning. Although many multi-label approaches have been proposed, few of them have considered to de-emphasize the effect of noisy features in the learning process. To address this issue, this paper designs a new method named representative multi-label learning algorithm. Instead of considering all features, the proposed algorithm focuses only on the representative ones, via incorporating an affinity propagation algorithm, kernel formulation, and a multi-label support vector machine into the learning framework. Specifically, it first adopts an affinity propagation algorithm to select a set of representative features and capture the relationships among features. Then, the algorithm constructs the representative kernel functions to measure the similarity between data instances. Finally, a multi-label support vector machine is applied to solve the learning problem. Based on the representative multi-label learning algorithm, we further design a representative multi-label learning ensemble framework to improve the accuracy, stableness, and robustness. Experimental results show that the proposed algorithm works well on most of the datasets and outperforms the compared multi-label learning approaches.
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