Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen}Loe`ve transform, is commonly used to reduce the dimensionality of a data set with a large number of interdependent variables. PCA is the optimal linear transformation with respect to minimizing the mean sq
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SubXPCA and a generalized feature partitioning approach to principal component analysis
β Scribed by Kadappagari Vijaya Kumar; Atul Negi
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 342 KB
- Volume
- 41
- Category
- Article
- ISSN
- 0031-3203
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