The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The bShowEBook copy of this book is not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.
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).