Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results
arXiv:2312.00296
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.
4 pages, 7 figures, KDD SoCal symposium 2023 (extended version)