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arXiv · 2312.00296

Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results

Biqian Cheng·Evangelos E. Papalexakis·Jia Chen
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Abstract

Canonical Correlation Analysis (CCA) has been widely applied to jointly embed multiple views of data in a maximally correlated latent space. However, the alignment between various data perspectives, which is required by traditional approaches, is unclear in many practical cases. In this work we propose a new framework Aligned Canonical Correlation Analysis (ACCA), to address this challenge by iteratively solving the alignment and multi-view embedding.

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Biqian Cheng, Evangelos E. Papalexakis, Jia Chen. 2023-12-01. Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results. https://arxiv.org/abs/2312.00296

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