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.

翻译：典型相关分析（CCA）已被广泛应用于将多视图数据联合嵌入到最大相关的潜在空间中。然而，传统方法所需的各种数据视角之间的对齐，在许多实际情况中尚不明确。本文提出一种新框架——对齐的典型相关分析（ACCA），通过迭代求解对齐与多视图嵌入来应对这一挑战。