Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results
Machine Learning
2023-12-11 v2 Machine Learning
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.
Keywords
Cite
@article{arxiv.2312.00296,
title = {Towards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results},
author = {Biqian Cheng and Evangelos E. Papalexakis and Jia Chen},
journal= {arXiv preprint arXiv:2312.00296},
year = {2023}
}
Comments
4 pages, 7 figures, KDD SoCal symposium 2023 (extended version)