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.

• Type: Conference
• Year: 2005
• Where: ICAPR
• Authors: Maxime Berar, Michel Desvignes, Gérard Bailly, Yohan Payan, Barbara Romaniuk
• Keywords: ICAPR 2005, Non Visible Parts, Observed Objects, Statistical Model