A Scalable Kernel-Based Algorithm for Semi-Supervised Metric Learning

In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale up well with the data set size. In this paper, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.