用双向泛化性能衡量数据集相似性，更精准指导迁移学习。
Quantifying Data Similarity Using Cross Learning
- 通过双向泛化能力评估数据集相似性，融合标签与特征响应信息。
- 在真实和合成数据上验证，能有效区分可迁移、模糊和不可迁移的源数据。
- 方法适用于深度模型，无需高维密度估计，易实现且具几何解释性。
数据集相似性度量在机器学习中至关重要，尤其对迁移学习和领域自适应而言。现有方法多基于输入特征分布，忽视标签信息与特征-响应对齐。为此，本文提出交叉学习得分（CLS），通过决策规则的双向泛化性能来量化数据集相似性。理论证明其与标准线性模型下决策边界间的余弦相似性相关，提供几何解释。设计了稳健的集成估计器，避免高维密度估计，易于实现。针对迁移学习，提出“可迁移区域”框架，将源数据划分为正向、模糊与负向迁移区。为适配深度学习，扩展CLS至编码器-头架构，契合现代表示学习流程。在合成与真实数据集上的大量实验验证了CLS在相似性度量与迁移评估中的有效性。
原文摘要 · Abstract (English)
Measuring dataset similarity is fundamental in machine learning, particularly for transfer learning and domain adaptation. In the context of supervised learning, most existing approaches quantify similarity of two data sets based on their input feature distributions, neglecting label information and feature-response alignment. To address this, we propose the Cross-Learning Score (CLS), which measures dataset similarity through bidirectional generalization performance of decision rules. We establish its theoretical foundation by linking CLS to cosine similarity between decision boundaries under canonical linear models, providing a geometric interpretation. A robust ensemble-based estimator is developed that is easy to implement and bypasses high-dimensional density estimation entirely. For transfer learning applications, we introduce a "transferable zones" framework that categorizes source datasets into positive, ambiguous, and negative transfer regions. To accommodate deep learning, we extend CLS to encoder-head architectures, aligning with modern representation-based pipelines. Extensive experiments on synthetic and real-world datasets validate the effectiveness of CLS for similarity measurement and transfer assessment.
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