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Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding
XiaodongWang; RungchingChen; ChaoqunHong; ZhiqiangZeng; ZhiliZhou
In this paper, we propose a novel semi-supervised multi-label feature selection algorithm and apply it to three different applications: natural scene classification, web page annotation, and yeast gene functional classification. Compared with the previous works, there are two advantages of our algorithm: (1) Manifold learning which leverages the underlying geometric structure of the training data is imposed to utilize both labeled and unlabeled data. Besides, the underlying manifold structure is guaranteed to be clear by using the l1-norm regularization. (2) Shared subspace learning which has shown its efficiency in multi-label learning scenarios, is also considered in our feature learning algorithm. The proposed objective function involves l2,1-norm and l1-norm, making it non-smooth and difficult to solve. We also design an efficient iterative algorithm to optimize it. Experimental results demonstrate the effectiveness of our algorithm compared with sate-of-the-art algorithms on different tasks.
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