Feature-aware Label Space Dimension Reduction for Multi-label Classification
Feature-aware Label Space Dimension Reduction for Multi-label Classification
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发表时间：
2012-12
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通讯作者：
Yao-Nan Chen;Hsuan-Tien Lin
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作者：
Yao-Nan Chen;Hsuan-Tien Lin
标签空间降维（LSDR）是一种高效的多类多标签分类方法。现有的LSDR方法，如压缩感知和主标签空间变换，只利用了数据集的标签部分，而没有利用特征部分。在本文中，我们提出了一种同时考虑标签和特征部分的LSDR新方法。该方法被称为条件主标签空间变换，基于最小化流行的汉明损失的上界。该方法的最小化步骤可以通过简单的奇异值分解来实现。此外，该方法还可以扩展为内核化版本，允许使用复杂的特性组合来辅助LSDR。实验结果表明，该方法在实际数据集上比现有方法更有效。
Label space dimension reduction (LSDR) is an efficient and effective paradigm for multi-label classification with many classes. Existing approaches to LSDR, such as compressive sensing and principal label space transformation, exploit only the label part of the dataset, but not the feature part. In this paper, we propose a novel approach to LSDR that considers both the label and the feature parts. The approach, called conditional principal label space transformation, is based on minimizing an upper bound of the popular Hamming loss. The minimization step of the approach can be carried out efficiently by a simple use of singular value decomposition. In addition, the approach can be extended to a kernelized version that allows the use of sophisticated feature combinations to assist LSDR. The experimental results verify that the proposed approach is more effective than existing ones to LSDR across many real-world datasets.