4 - Kernel Methods
Published online by Cambridge University Press: 30 June 2022
Summary
This chapter discusses the fundamental kernel methods, with applications to supervised (kernel ridge regression or LS-SVM), semi-supervised (graph Laplacian-based learning), or unsupervised learning (such as kernel spectral clustering) schemes. By focusing on the typical examples of distance and inner-product-type kernels, we show how large-dimensional kernel approach differs from our small-dimensional intuition, and perhaps more importantly, how random matrix theory plays a central role in understanding and tuning various kernel-based methods.
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- Random Matrix Methods for Machine Learning , pp. 207 - 276Publisher: Cambridge University PressPrint publication year: 2022