An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines

The concept of semi-supervised support vector machines extends classical support vector machines to learning scenarios, where both labeled and unlabeled patterns are given. In recent years, such semi-supervised extensions have gained considerable attention due to their huge potential for real-world applications with only small amounts of labeled data. While being appealing from a practical point of view, semi-supervised support vector machines lead to a combinatorial optimization problem that is difficult to address. Many optimization approaches have been proposed that aim at tackling this task. However, the computational requirements can still be very high, especially in case large data sets are considered and many model parameters need to be tuned. A recent trend in the field of big data analytics is to make use of graphics processing units to speed up computationally intensive tasks. In this work, such a massively-parallel implementation is developed for semi-supervised support vector machines. The experimental evaluation, conducted on commodity hardware, shows that valuable speed-ups of up to two orders of magnitude can be achieved over a standard single-core CPU execution.