A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification
This work addresses scalability issues in SVM classification for machine learning practitioners, but it is incremental as it adapts an existing technique to a new context.
The paper tackles the problem of improving Frank-Wolfe algorithms for large-scale SVM classification by introducing a PARTAN variant, showing promising results on benchmark datasets.
Frank-Wolfe algorithms have recently regained the attention of the Machine Learning community. Their solid theoretical properties and sparsity guarantees make them a suitable choice for a wide range of problems in this field. In addition, several variants of the basic procedure exist that improve its theoretical properties and practical performance. In this paper, we investigate the application of some of these techniques to Machine Learning, focusing in particular on a Parallel Tangent (PARTAN) variant of the FW algorithm that has not been previously suggested or studied for this type of problems. We provide experiments both in a standard setting and using a stochastic speed-up technique, showing that the considered algorithms obtain promising results on several medium and large-scale benchmark datasets for SVM classification.