Archivio Istituzionale della RicercaProper Orthogonal Decomposition (POD) is a method with much potential for identifying, locating and quantifying damage in structures [1-3]. POD can be interpreted as the maximum-likelihood solution to a probabilistic model called Probabilistic Principal Component Analysis (PPCA) [4]. Previous work in the Machine Learning community, especially [5], has shown that PPCA (and therefore also POD) is a member of a larger family algorithms, linear Gaussian models. The primary objective of the work is to demonstrate that POD is the solution to a probabilistic model; he benefits of viewing POD in this way are discussed as the basis for future research.
Probabilistic Proper Orthogonal Decomposition / Hensman, J; Gherlone, Marco; Surace, Cecilia; DI SCIUVA, Marco. - STAMPA. - (2010), pp. 907-912. (Intervento presentato al convegno V European Workshop on structural health monitoring tenutosi a Sorrento nel June 29 – July 2, 2010).
Probabilistic Proper Orthogonal Decomposition
GHERLONE, Marco;SURACE, Cecilia;DI SCIUVA, Marco
2010
Abstract
Proper Orthogonal Decomposition (POD) is a method with much potential for identifying, locating and quantifying damage in structures [1-3]. POD can be interpreted as the maximum-likelihood solution to a probabilistic model called Probabilistic Principal Component Analysis (PPCA) [4]. Previous work in the Machine Learning community, especially [5], has shown that PPCA (and therefore also POD) is a member of a larger family algorithms, linear Gaussian models. The primary objective of the work is to demonstrate that POD is the solution to a probabilistic model; he benefits of viewing POD in this way are discussed as the basis for future research.
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https://hdl.handle.net/11583/2360580
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