Fast Newton Nearest Neighbors Boosting For Image Classification

International audienceRecent works display that large scale image classification problems rule out computationally demanding methods. On such problems, simple approaches like <i>k</i>-NN are affordable contenders, with still room space for statistical improvements under the algorithmic constraints. A recent work showed how to leverage <i>k</i>-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 <i>k</i>-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