In this paper, we investigate the full equivalence, under basic conditions, between the Probabilistic PCA clustering approach and the reconstruction of signal subspaces based on the singular value decomposition. Therefore this equivalence allows the adaptive determination of the clusters identified on data, in order to maximize the quality of the reconstructed signal. Furthermore, using known results in SVD framework, we also introduce a new technique to estimate automatically the dimension of the latent variable subspace.
A probabilistic PCA clustering approach to the SVD estimate of signal subspaces / Panella, Massimo; G., Grisanti; Rizzi, Antonello. - STAMPA. - (2005), pp. 271-279. ((Intervento presentato al convegno 15th Italian Workshop on Neural Nets (WIRN VETRI 2004) tenutosi a Perugia, ITALY nel SEP 14-17, 2004. [10.1007/1-4020-3432-6_32].
A probabilistic PCA clustering approach to the SVD estimate of signal subspaces
PANELLA, Massimo;RIZZI, Antonello
2005
Abstract
In this paper, we investigate the full equivalence, under basic conditions, between the Probabilistic PCA clustering approach and the reconstruction of signal subspaces based on the singular value decomposition. Therefore this equivalence allows the adaptive determination of the clusters identified on data, in order to maximize the quality of the reconstructed signal. Furthermore, using known results in SVD framework, we also introduce a new technique to estimate automatically the dimension of the latent variable subspace.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
