Riesz–Haviland criterion for incomplete data

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

image reconstruction from incomplete data

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An Akaike Information Criterion for Model Selection in the Presence of Incomplete Data Title: an Aic for Model Selection with Incomplete Data

We derive and investigate a variant of AIC, the Akaike information criterion, for model selection in settings where the observed data is incomplete. Our variant is based on the motivation provided for the PDIO (\predictive divergence for incomplete observation models") criterion of Shimodaira variant diiers from PDIO in its \goodness-of-t" term. Unlike AIC and PDIO, which require the computatio...