Abstract: In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respects the provided side-information as well as the local geometry of the data. Our framework is based on metric learning methods, where we jointly model the metric/kernel over the data along with the underlying manifold. Furthermore, we show that for some important parameterized forms of the underlying manifold model, we can estimate the model parameters and the kernel matrix efficiently. Our resulting algorithm is able to incorporate local geometry into the metric learning task; at the same time it can handle a wide class of constraints. Finally, our algorithm is fast and scalable – unlike most of the existing methods, it is able to exploit the low dimensional manifold structure and does not require semi-definite programming. We demonstrate wide applicability and effectiveness of our framework by applying to various machine learning tasks such as semisupervised classification, colored dimensionality reduction, manifold alignment etc. On each of the tasks our method performs competitively or better than the respective state-of-the-art method.
- Metric Learning