Missing data estimation with statistical models

In this paper, we deal with the pattern recognition problem using non-linear statistical models based on Kernel Principal Component Analysis. Objects that we try to recognize are defined by ordered sets of points. We present here two types of models: the first one uses an explicit projection function, the second one uses the Kernel trick. The present work attempts to estimate the localization of partially visible objects. Both are applied to the cephalometric problem with good results.

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