What is CO-PCA?

CO-PCA, or Coupled Object-PCA, is a variant of Principal Component Analysis (PCA) designed to analyze data where objects are represented by multiple feature sets (views). Traditional PCA is applied to each view separately, but CO-PCA seeks to find a shared, lower-dimensional representation that captures the correlated information across all views. This is achieved by enforcing constraints that couple the principal components derived from different views, ensuring they are related. It's commonly used in applications where data consists of multiple modalities or feature sets describing the same underlying objects, such as image and text data, or genomic and proteomic data.