文件名称:progarmlab4
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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相关搜索： feature
dimension
reduction
pca
data
dimension
FEATURE
SELECTION
RELIEF
feature
extraction
principal
component
analysis
feature
extraction
information
theory
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相关搜索： feature
dimension
reduction
pca
data
dimension
FEATURE
SELECTION
RELIEF
feature
extraction
principal
component
analysis
feature
extraction
information
theory
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