Thomas Gärtner, Quoc Le, Simon Burton, Alex J. Smola, Vishy Vishwanathan
We present a method for performing transductive inference on very large datasets. Our algorithm is based on multiclass Gaussian processes and is effective whenever the multiplication of the kernel matrix or its inverse with a vector can be computed sufﬁciently fast. This holds, for instance, for certain graph and string kernels. Transduction is achieved by varia- tional inference over the unlabeled data subject to a balancing constraint.