VARIABLE SELECTION AND INTERPRETATION OF COVARIANCE PRINCIPAL COMPONENTS
β Scribed by Al-Kandari, Noriah M.; Jolliffe, Ian T.
- Book ID
- 120836933
- Publisher
- Taylor and Francis Group
- Year
- 2001
- Tongue
- English
- Weight
- 162 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0361-0918
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π SIMILAR VOLUMES
In many large environmental datasets redundant variables can be discarded without the loss of extra variation. Principal components analysis can be used to select those variables that contain the most information. Using an environmental dataset consisting of 36 meteorological variables spanning 37 y
## Abstract Component extraction techniques are used widely in the analysis and interpretation of highβdimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representa