PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pozza S. Kernel PCA for novelty detection. Technical report, 2017.
 
 
Abstract
(English)
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.
Subject Kernel principal component analysis
Novelty detection
Structural health monitoring
J.2 PHYSICAL SCIENCES AND ENGINEERING


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