文摘
When feature measurements are stored in a distributed fashion, such as in sensor networks, learning a support vector machine (SVM) with a full kernel built with accessing all features can be pricey due to required communication. If we build an individual SVM for each subset of features stored locally, then the SVMs may behave quite differently, being unable to capture global trends. However, it is possible to make the individual SVMs behave nearly the same, using a simple yet effective idea we propose in this paper. Our approach makes use of two kernel matrices in each node of a network, a local kernel matrix built with only locally stored features, and an estimate of remote information (about “local” kernels stored in the other nodes). Using matrix completion, remote information is fully recovered with high probability from a small set of sampled entries. Due to symmetric construction, each node will be equipped with nearly identical kernel matrices, and therefore individually trained SVMs on these matrices are expected to have good consensus. Experiments showed that such SVMs trained with relatively small numbers of sampled remote kernel entries have competent prediction performance to full models.