Abstract--This paper presents a framework for privacypreserving Gaussian Mixture Model computations. Specifically, we consider a scenario where a central service wants to learn the...
Randomization is an economical and efficient approach for privacy preserving data mining (PPDM). In order to guarantee the performance of data mining and the protection of individ...
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
Constantinos Constantinopoulos, Michalis K. Titsia...
With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy conc...
In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is a new, edge preserving, smoothness prior whic...
Giorgos Sfikas, Christophoros Nikou, Nikolas P. ...