Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding
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DOI：
10.1016/j.imavis.2017.05.004
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Xiaodong Wang;R. Chen;Chao-qun Hong;Zhi-qiang Zeng;Zhi-li Zhou
中科院分区：
文献类型：
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作者：
Xiaodong Wang;R. Chen;Chao-qun Hong;Zhi-qiang Zeng;Zhi-li Zhou
本文提出了一种新的半监督多标签特征选择算法，并将其应用于自然场景分类、网页标注和酵母基因功能分类三种不同的应用中。与以往的工作相比，我们的算法有两个优点：(1)利用训练数据的底层几何结构进行流形学习，可以同时利用标记和未标记的数据。此外，利用1范数正则化保证了底层流形结构的清晰性。(2)我们的特征学习算法也考虑了共享子空间学习在多标签学习场景下的有效性。所提出的目标函数涉及l2、1-范数和11 -范数，使得其不光滑且难以求解。并设计了一种高效的迭代算法对其进行优化。实验结果表明，在不同的任务下，该算法与现有算法相比是有效的。
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 thel1-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 involvesl2,1-norm andl1-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.