Probabilistic Principal Component Analysis
β Scribed by Michael E. Tipping; Christopher M. Bishop
- Book ID
- 108547510
- Publisher
- Blackwell Publishing
- Year
- 1999
- Tongue
- English
- Weight
- 175 KB
- Volume
- 61
- Category
- Article
- ISSN
- 0952-8385
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several interβcorrelated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new or
The theoretical principles and practical implementation of a new method for multivariate data analysis, maximum likelihood principal component analysis (MLPCA), are described. MLCPA is an analog to principal component analysis (PCA) that incorporates information about measurement errors to develop P
In this article, which appeared in Volume 15(9), 963-980, there were several typesetting errors in the equations. The corrected equations appear below. ## PRINCIPAL COMPONENT ANALYSIS measure of the overall molecular structural response to parametric disturbances, dp. ## The log normalized sensi