Fast Newton Nearest Neighbors Boosting For Image Classification

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.
Keywords : Machine learning
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Communication dans un congrès
MLSP - 23rd Workshop on Machine Learning for Signal Processing, Sep 2013, Southampton, United Kingdom. IEEE, pp.6, 2013
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Wafa Bel Haj Ali, Richard Nock, Franck Nielsen, Michel Barlaud. Fast Newton Nearest Neighbors Boosting For Image Classification. MLSP - 23rd Workshop on Machine Learning for Signal Processing, Sep 2013, Southampton, United Kingdom. IEEE, pp.6, 2013. 〈hal-00959125〉

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