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+Sebastian Raschka, 2015
+
+Python Machine Learning - Code Examples
+
+## Chapter 5 - Compressing Data via Dimensionality Reduction
+
+- Unsupervised dimensionality reduction via principal component analysis 128
+  - Total and explained variance
+  - Feature transformation
+  - Principal component analysis in scikit-learn
+- Supervised data compression via linear discriminant analysis
+  - Computing the scatter matrices
+  - Selecting linear discriminants for the new feature subspace
+  - Projecting samples onto the new feature space
+  - LDA via scikit-learn
+- Using kernel principal component analysis for nonlinear mappings
+  - Kernel functions and the kernel trick
+  - Implementing a kernel principal component analysis in Python
+    - Example 1 – separating half-moon shapes
+    - Example 2 – separating concentric circles
+  - Projecting new data points
+  - Kernel principal component analysis in scikit-learn
+- Summary