Description: The Principal component analysis, is a standard technique used for data reduction in statistical pattern recognition and signal processing
A common problem in statistical pattern recognition is feature selection or feature extraction. Feature selection is a process whereby a data space is transformed into a feature space that theory has exactly same dimension as the original data space. However the transformation is designed in such a way that the data set is represented by a reduced number of “effective features” and most of the intrinsic information content of the data or the data set undergoes a dimensionality reduction.
PCA
- [Corner] - The image feature extraction in order to
- [pca_analysis] - Procedure Note: y = pca (mixedsig), the
- [pcass] - This is an efficient implementation of P
- [Output] - a complete paper GA based feature select
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