Top-k Multiclass SVM
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.
In many multiclass classification problems with a large number of classes, semantic similarity between classes can typically be observed. This paper proposed an algorithm called top-k SVM to explicitly deal with the learning scenario where you only get a cost when the ground truth is outside the top k proposed classes (e.g. imagenet challenge). After extending the loss function to top-k loss, an efficient optimization procedure is formulated. Experiments on large scale datasets showed the scalability of the algorithm and clear improvement could be observed over compared algorithms.The paper is clearly motivated and easy to follow. And the problem (similar classes in multiclass learning) they work on is an important one that happens a lot in many practical applications.