Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods by Chris Aldrich & Lidia Auret

Author:Chris Aldrich & Lidia Auret
Language: eng
Format: epub
Publisher: Springer London, London

Given a specific kernel function k, calculate the kernel K and centred kernel matrices of the centred training data. (Kernel function parameters can be determined by cross-validation, where the cross-validation criterion may be mean squared reconstruction error.)

Eigendecomposition of returns r ordered eigenvectors P and with non-zero eigenvalues Λ.

The first D kernel principal components are retained as the projection vectors P*, where the selection of D is based on some selection criteria (e.g. certain fraction of variance accounted for).

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