Kernel Principal Component Analysis (PCA): Explained with an Example MarkTechPost

 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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