Despite the introduction of constrained principal component analysis (CPCA) over 20 years ago, there is no single resource that examines its ramifications, extensions, implementations, and applications. This book explores how CPCA incorporates external information into PCA of a main data matrix. It provides a systematic, in-depth account of the mathematical underpinnings, special cases, related topics, interesting applications, and implementation details. The author explains how CPCA first decomposes the data matrix according to the external information (external analysis) and then applies PCA to decomposed matrices (internal analysis).