ILLUSTRATIVE EXAMPLES OF PRINCIPAL COMPONENTS ANALYSIS
β Scribed by W.T. FEDERER; C.E. MCCULLOCH; N.J. MILES-MCDERMOTT
- Book ID
- 111344677
- Publisher
- John Wiley and Sons
- Year
- 1987
- Tongue
- English
- Weight
- 573 KB
- Volume
- 2
- Category
- Article
- ISSN
- 0887-8250
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
Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques it continues to be the subject of much research, ranging from new model- based approaches to algorithmic ideas from neural networks. It is extremely versatile with applicat
Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra.
A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series