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18 lines
758 B
TeX
18 lines
758 B
TeX
Unsupervised dimensionality reduction via principal component analysis 128
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Total and explained variance
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Feature transformation
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Principal component analysis in scikit-learn
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Supervised data compression via linear discriminant analysis
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Computing the scatter matrices
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Selecting linear discriminants for the new feature subspace
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Projecting samples onto the new feature space
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LDA via scikit-learn
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Using kernel principal component analysis for nonlinear mappings
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Kernel functions and the kernel trick
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Implementing a kernel principal component analysis in Python
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Example 1 – separating half-moon shapes
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Example 2 – separating concentric circles
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Projecting new data points
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Kernel principal component analysis in scikit-learn
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Summary
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