You're reading from Python Machine Learning - Third Edition

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Published inDec 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789955750

Edition3rd Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

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Using kernel principal component analysis for nonlinear mappings

Many machine learning algorithms make assumptions about the linear separability of the input data. You have learned that the perceptron even requires perfectly linearly separable training data to converge. Other algorithms that we have covered so far assume that the lack of perfect linear separability is due to noise: Adaline, logistic regression, and the (standard) SVM to just name a few.

However, if we are dealing with nonlinear problems, which we may encounter rather frequently in real-world applications, linear transformation techniques for dimensionality reduction, such as PCA and LDA, may not be the best choice.

In this section, we will take a look at a kernelized version of PCA, or KPCA, which relates to the concepts of kernel SVM that you will remember from Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn. Using KPCA, we will learn how to transform data that is not linearly separable...

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Python Machine Learning - Third Edition

Published in: Dec 2019Publisher: PacktISBN-13: 9781789955750

Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Read more about Vahid Mirjalili

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Using kernel principal component analysis for nonlinear mappings

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