📋 Kernel Principal Component Analysis (PCA): Explained with an Example 완벽가이드
✨ Kernel Principal Component Analysis (PCA): Explained with an Example
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Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets such as two moons: PCA flattens the structure and mixes the classes together. Kernel PCA fixes
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Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets such as two moons: PCA flattens the structure and mixes the classes together. Kernel PCA fixes this limitation by mapping the data into a higher-dimensional feature space where nonlinear […]
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