Constrained Principal Component Analysis and Related Techniques

CRC Press

Constrained Principal Component Analysis and Related TechniquesAuthor(s): Yoshio Takane\nFormat: Hardback\nPublisher: Taylor & Francis Inc, United States\nImprint: CRC Press Inc\nISBN-13: 9781466556669, 978-1466556669\nSynopsis\nIn multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. \n\nHow can regression analysis and PCA be combined in a beneficial way? \nWhy and when is it a good idea to combine them? \nWhat kind of benefits are we getting from them? \n\nAddressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.\n\nThe book begins with four concrete examples of CPCA that provide readers with a basic understanding of the technique and its applications. It gives a detailed account of two key mat.