Nonlinear Principal Component Analysis and Its Applications

Discover the groundbreaking insights in "Nonlinear Principal Component Analysis and Its Applications" by Yuichi Mori, published by Springer Verlag in 2016. This first edition, spanning 80 pages, delves into the principles of nonlinear principal component analysis (PCA), a powerful technique for analyzing data with mixed measurement levels. Mori expertly explains the theoretical foundations and practical applications of this innovative method, making it an essential read for researchers and practitioners in data analysis. Enhance your understanding of PCA and its versatility in various fields with this comprehensive guide. Perfect for those looking to deepen their analytical skills and apply nonlinear techniques to their own data challenges.