Fast Newton Nearest Neighbors Boosting For Image Classification
Wafa Bel Haj Ali, Richard Nock, Frank Nielsen, Michel Barlaud

Abstract:
Recent works display that large scale image classification problems rule out computationally demanding methods. On such problems, simple approaches like k-NN are affordable contenders, with still room space for statistical improvements under the algorithmic constraints. A recent work showed how to leverage k-NN to yield a formal boosting algorithm. This method, however, has numerical issues that make it not suited for large scale problems.
We propose here an Adaptive Newton-Raphson scheme to leverage k-NN, N3 , which does not suffer these issues. We show that it is a boosting algorithm, with several key algorithmic and statistical properties. In particular, it may be sufficient to boost a subsample to reach desired bounds for the loss at hand in the boosting framework. Experiments are provided on the SUN, and Caltech databases. They confirm that boosting a subsample sometimes containing few examples only is sufficient to reach the convergence regime of N3 . Under such conditions, N3 challenges the accuracy of contenders with lower computational cost and lower memory
requirement.