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