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ICAPR
2005
Springer

Missing Data Estimation Using Polynomial Kernels

10 years 4 months ago
Missing Data Estimation Using Polynomial Kernels
Abstract. In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statistical model. We present two models in this paper: a linear model based on PCA and a non-linear model based on KPCA. The present work attempts to localize of non visible parts of an object, from the visible part and from the model, using the variability represented by the models. Both are applied to synthesis data and to cephalometric data with good results.
Maxime Berar, Michel Desvignes, Gérard Bail
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICAPR
Authors Maxime Berar, Michel Desvignes, Gérard Bailly, Yohan Payan, Barbara Romaniuk
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