Top-k Multiclass SVM

Maksim Lapin; Matthias Hein; Bernt Schiele

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arXiv · 1511.06683

Top-k Multiclass SVM

Maksim Lapin·Matthias Hein·Bernt Schiele

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Abstract

Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimize for top-k performance. Our generalization of the well-known multiclass SVM is based on a tight convex upper bound of the top-k error. We propose a fast optimization scheme based on an efficient projection onto the top-k simplex, which is of its own interest. Experiments on five datasets show consistent improvements in top-k accuracy compared to various baselines.

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Maksim Lapin, Matthias Hein, Bernt Schiele. 2015-11-20. Top-k Multiclass SVM. https://arxiv.org/abs/1511.06683

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