提出DSE方法，精准识别跨群体分类中最具区分性的特征子空间。
Discriminative Subspace Emersion from learning feature relevances across different populations
- 通过学习不同群体的特征相关性，自动提取判别性强的特征子空间。
- 在类别重叠率极高的情况下仍能准确识别共通判别子空间。
- 适用于多标签任务，不依赖特定分类器，适合可解释模型研究者。
在分类任务中，模型性能常受训练集有限、特征维度高及类别间重叠严重的影响。对于具有（分段）线性决策边界的可解释学习模型，可通过优化设计或特征相关性估计缓解上述问题。当任务在两个独立群体间共享时，核心目标转向识别在跨群体分类中最具区分性的特征集合。本文提出一种新的判别子空间涌现（Discriminative Subspace Emersion, DSE）方法，将子空间学习扩展为通用的相关性学习框架。DSE 能在类别高度重叠的情况下，仍准确识别出两个群体间共有的判别特征子空间。该方法支持多标签设置，理论与实证分析均表明其在合成数据和真实数据集上表现优异，即使在极高类别重叠度下仍能有效定位关键特征子空间。
原文摘要 · Abstract (English)
In a given classification task, the accuracy of the learner is often hampered by finiteness of the training set, high-dimensionality of the feature space and severe overlap between classes. In the context of interpretable learners, with (piecewise) linear separation boundaries, these issues can be mitigated by careful construction of optimization procedures and/or estimation of relevant features for the task. However, when the task is shared across two disjoint populations the main interest is shifted towards estimating a set of features that discriminate the most between the two, when performing classification. We propose a new Discriminative Subspace Emersion (DSE) method to extend subspace learning toward a general relevance learning framework. DSE allows us to identify the most relevant features in distinguishing the classification task across two populations, even in cases of high overlap between classes. The proposed methodology is designed to work with multiple sets of labels and is derived in principle without being tied to a specific choice of base learner. Theoretical and empirical investigations over synthetic and real-world datasets indicate that DSE accurately identifies a common subspace for the classification across different populations. This is shown to be true for a surprisingly high degree of overlap between classes.
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