rasbt--python-machine-learning-book
wehub-resource-sync
23 行
919 B
Markdown
23 行
919 B
Markdown
Sebastian Raschka, 2015
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Python Machine Learning - Code Examples
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## Chapter 5 - Compressing Data via Dimensionality Reduction
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- 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 |