When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?
When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?
Abstract Principal component analysis is a popular dimension reduction technique often used to visualize high‐dimensional data structures. In genomics, this can involve millions of variables, but only tens to hundreds of observations. Theoretically, such extreme high dimensionality will cause biased or inconsistent eigenvector estimates, but in practice, the principal component …