Problem
Research questions and friction points this paper is trying to address.
Canonical Correlation Analysis
Unpaired data
Multiview learning
Quadratic Assignment Problem
Unpaired Canonical Correlation Analysis
🤖 AI Summary
This study addresses the heavy reliance of traditional Canonical Correlation Analysis (CCA) on paired data, which is often difficult to obtain in practical cross-modal scenarios. To overcome this limitation, this work proposes an unsupervised method termed UCCA. By establishing a theoretical connection between the Quadratic Assignment Problem (QAP) and CCA, the authors derive an optimization algorithm that operates without paired samples, utilizing linear projections to learn a shared feature space from unpaired multimodal data that maximizes true correlations. This research achieves maximum correlation projection learning under strictly unpaired settings for the first time, bridging a critical theoretical gap between multi-view statistical learning and unpaired data learning. Furthermore, extensive experiments demonstrate that UCCA significantly outperforms existing baselines on real-world multimodal datasets, efficiently recovering underlying true correlations.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unpaired Canonical Correlation Analysis
Quadratic Assignment Problem
Multiview Learning
Unpaired Data
Linear Projections
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