Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Kernel PCA Versus the Rest

The video tutorial explores kernel PCA, a method for dimensionality reduction that uses a kernel matrix to encode pairwise similarities between data points. It discusses the challenges of reconstruction and kernel selection, and introduces neighborhood methods like MDDS, LLE, and Laplacian eigenmaps. The tutorial also covers maximum variance unfolding, which learns the kernel from data, and concludes with a discussion on supervised dimensionality reduction techniques.

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