Semi-Supervised Learning through Principal Directions Estimation

We describe methods for taking into account unlabeled data in the training of a kernel-based classifier, such as a Support Vector Machines (SVM). We propose two approaches utilizing unlabeled points in the vicinity of labeled ones. Both of the approaches effectively modify the metric of the pattern space, either by using nonspherical Gaussian density estimates which are determined using EM, or by modifying the kernel function using displacement vectors computed from pairs of unlabeled and labeled points. The latter is linked to techniques for training invariant SVMs. We present experimental results indicating that the proposed technique can lead to substantial improvements of classification accuracy.