A User's Guide to Principal Components (Wiley Series in Probability and Statistics)

Rating:
Summary: Principal Components Analysis
Review: This book is an excellent choice for helping understand data compression and noise reduction of large datasets. It is extremely beneficial, especially when dealing with hyperspectral datasets, to understand the techniques involving the transformation of multiple bands into principal components. The book is well organized according to the general method(s) by which PCA works. From the compression of information content in a multiple number of bands, to other uses of principle components analysis, this is definitely an excellent reference for anyone who works with hyperspectral data.

Rating:
Summary: Principal Components Analysis
Review: This book is an excellent choice for helping understand data compression and noise reduction of large datasets. It is extremely beneficial, especially when dealing with hyperspectral datasets, to understand the techniques involving the transformation of multiple bands into principal components. The book is well organized according to the general method(s) by which PCA works. From the compression of information content in a multiple number of bands, to other uses of principle components analysis, this is definitely an excellent reference for anyone who works with hyperspectral data.

Rating:
Summary: Not good for finance
Review: This book is geared toward engineering types, not for people who want to use PCA for stock trading, securities covariance forecasting, etc.