Description: PCA (Principal Component Analysis) is not just for high-dimensional data dimensionality reduction, more importantly, is the result of dimensionality reduction removes noise and found patterns in the data. PCA of the original n features with less number of m feature substitution, the new feature is a linear combination of the old features, which maximize a linear combination of the sample variance, try to make the new features of m uncorrelated. Mapping feature to capture data the old to the new features inherent variability.
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PCA
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