2017
Report  Open Access

Kernel PCA for novelty detection

Pozza S.

Kernel principal component analysis  Novelty detection  Structural health monitoring 

Novelty detection indexes are used in order to identify anomaly in the observation of a phenomenon. We describe the basic idea of kernel principal component analysis, a method which enlightens the existence of a novelty in a measured value comparing it with the one predicted by a model calibrated on training data. Differently from linear PCA, kernel PCA projects the data into an infinite-dimensional space in which novelty detection has usually a better performance.

Source: ISTI Technical reports, 2017



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BibTeX entry
@techreport{oai:it.cnr:prodotti:365431,
	title = {Kernel PCA for novelty detection},
	author = {Pozza S.},
	institution = {ISTI Technical reports, 2017},
	year = {2017}
}