Investigating correlation between example features and example labels is essential to the solving of classification problems. However, identification and calculation of the correlation between features and labels can be rather difficult in case involving high-dimensional multi-label data. Both feature embedding and label embedding have been developed to tackle this challenge, and a shared subspace for both labels and features is usually learned by applying existing embedding methods to simultaneously reduce the dimension of features and labels. By contrast, this paper suggests learning separate subspaces for features and labels by maximizing the independence between the components in each subspace, as well as maximizing the correlation between these two subspaces. The learned independent label components indicate the fundamental combinations of labels in multi-label datasets, which thus helps to reveal the correlation between labels. Furthermore, the learned independent feature components lead to a compact representation of example features. The connections between the proposed algorithm and existing embedding methods are discussed in detail. Experimental results on real-world multi-label datasets demonstrate that it is necessary for us to explore independent components from multi-label data, and further prove the effectiveness of the proposed algorithm.

中文翻译：

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一种创新的基于多标签学习的城市数据计算算法

研究示例特征与示例标签之间的相关性对于解决分类问题至关重要。然而，在涉及高维多标签数据的情况下，特征和标签之间的相关性的识别和计算可能相当困难。已经开发了特征嵌入和标签嵌入来解决这一难题，并且通常通过应用现有嵌入方法同时减小特征和标签的尺寸来学习标签和特征的共享子空间。相比之下，本文建议通过最大化每个子空间中组件之间的独立性，以及最大化这两个子空间之间的相关性，来学习特征和标签的单独子空间。所学习的独立标签成分指示了多标签数据集中标签的基本组合，从而有助于揭示标签之间的相关性。此外，所学习的独立特征分量导致示例特征的紧凑表示。详细讨论了所提出算法与现有嵌入方法之间的联系。在现实世界中的多标签数据集上的实验结果表明，我们有必要从多标签数据中探索独立的组成部分，并进一步证明该算法的有效性。详细讨论了所提出算法与现有嵌入方法之间的联系。在现实世界中的多标签数据集上的实验结果表明，我们有必要从多标签数据中探索独立的组成部分，并进一步证明该算法的有效性。详细讨论了所提出算法与现有嵌入方法之间的联系。在现实世界中的多标签数据集上的实验结果表明，我们有必要从多标签数据中探索独立的组成部分，并进一步证明该算法的有效性。