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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A general theory of a class of linear neural nets for principal and minor component analysis
โ Scribed by Kiyotoshi Matsuoka
- Publisher
- Springer Japan
- Year
- 1999
- Tongue
- English
- Weight
- 723 KB
- Volume
- 3
- Category
- Article
- ISSN
- 1433-5298
No coin nor oath required. For personal study only.
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