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Optimizing Top-k Multiclass SVM via Semismooth Newton Algorithm
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DOI：10.1109/TNNLS.2018.2826039.png)
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Top-k performance has recently received increasing attention in large data categories. Advances, like a top-k multiclass support vector machine (SVM), have consistently improved the top-k accuracy. However, the key ingredient in the state-of-the-art optimization scheme based upon stochastic dual coordinate ascent relies on the sorting method, which yields O(d log d) complexity. In this paper, we leverage the semismooth-ness of the problem and propose an optimized top-k multiclass SVM algorithm, which employs semismooth Newton algorithm for the key building block to improve the training speed. Our method enjoys a local superlinear convergence rate in theory. In practice, experimental results confirm the validity. Our algorithm is four times faster than the existing method in large synthetic problems; Moreover, on real-world data sets it also shows significant improvement in training time.
Keyword:
Multiclass support vector machine (SVM)
root finding
semismooth Newton method
stochastic dual coordinate ascent (SDCA) optimization
top-k error
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