The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data

There has been substantial progress in the past decade in the development of object classifiers for images, for example of faces, humans and vehi- cles. Here we address the problem of contaminations (e.g. Variational inference is used to marginalize over contamination and obtain robust classification. In this way the VIC ap- proach can turn a kernel classifier for clean data into one that can tolerate contamination, without any specific training on contaminated positives. Recent progress in discriminative object detection, especially for faces, has yielded good performance and efficiency [1, 2, 3, 4].