Linear Dimensionality Reduction: An Approach Based on Scatter Matrices

By Klaus Nordhausen and Hannu Oja

In almost all fields of applications, statisticians are confronted with highly complex data sets with large dimensions. Dimension reduction methods naturally provide a better understanding of the data and reveal hidden structures. The book provides tools with a unifying statistical theory to recover hidden structures, latent variables, or latent subspaces in multivariate and dependent data. Throughout the book, the theory is illustrated with examples on practical data sets.

• Format: Hardcover
• Language: English
• ISBN: 1118494628
• ISBN13: 9781118494622
• Release Date: May 2022
• Publisher: Wiley
• Length: 420 Pages