发表机构
Bar-Ilan University; Technion（巴伊兰大学; 以色列理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出非配对典型相关分析（UCCA），在无配对样本时学习线性投影以最大化真实配对相关性，填补了传统多视图学习与非配对数据学习的空白。
AI 中文摘要
典型相关分析（CCA）是多视图共享空间学习的基本方法。然而，它对配对数据的严格依赖构成了重大限制，因为此类数据往往难以获取或完全不可用。在本文中，我们提出了非配对典型相关分析（UCCA），一种新颖的方法，它学习线性投影以最大化真实底层配对的相关性，而无需在训练期间访问任何配对样本。我们首先建立了将二次分配问题（QAP）与CCA联系起来理论结果。利用这些理论见解，我们推导出一种仅从非配对数据中最大化相关性的实用方法。据我们所知，UCCA是第一种在严格非配对设置中学习最大相关投影的方法。我们在真实世界的多模态数据集上验证了UCCA，表明它在恢复底层真实相关性方面显著优于最近的非配对对齐基线。这项工作填补了传统统计多视图学习与日益增长的非配对数据学习领域之间的关键空白。
英文摘要
Canonical Correlation Analysis (CCA) is a fundamental method for multiview shared space learning. However, its strict reliance on paired data poses a significant limitation, as such data is often difficult to obtain or entirely unavailable. In this paper, we present Unpaired CCA (UCCA), a novel method that learns linear projections to maximize the correlation of the true underlying pairing without access to any paired samples during training. We first establish theoretical results connecting the Quadratic Assignment Problem (QAP) to CCA. Leveraging these theoretical insights, we derive a practical method to maximize correlation exclusively from unpaired data. To the best of our knowledge, UCCA is the first approach to learn maximally correlated projections in a strictly unpaired setting. We validate UCCA on real-world multi-modal datasets, demonstrating that it significantly outperforms recent unpaired alignment baselines in recovering the underlying true correlation. This work fills a critical gap between traditional statistical multiview learning and the growing field of unpaired data learning.
CommentsAccepted to NeurIPS 2026
