2023/12/01 by Biqian Cheng, Evangelos E. Papalexakis, Cheng, Biqian +3
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2312.00296
openalex publication_date 2023/12/01 · openalex created_date 2023/12/05 · openalex updated_date 2026/07/28
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.