In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection.

中文翻译：

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基于深度典型相关分析的联合多标签分类与特征选择

近年来，多标签学习已被应用于许多应用领域，但仍是一项具有挑战性的任务。在多标签学习中，一个实例通常同时属于多个类标签。标签之间通常存在相关性，挖掘标签相关性有助于提高多标签分类性能。为了提高预测的准确性，标签嵌入（LE）是一项重要的技术，有利于提取标签信息进行多标签学习。在本文中，我们通过利用标签相关性提出了一种新的多标签学习方法，该方法可以自然地扩展到解决特征选择问题。首先，为了获得所有标签共享的判别特征，所提出的算法通过采用深度典型相关分析来学习潜在空间。然后我们通过强制对相似标签的预测相似来利用标签相关性，从而提高预测性能。在多个多数据集上的结果表明，该算法在多标签分类和特征选择方面具有优势。