Channel-Robust Classifiers

A key assumption underlying traditional supervised learning algorithms is that labeled examples used to train a classifier are drawn i.i.d. from the same distribution as test samples. This assumption is violated when classifying a test sample whose statistics differ from the training samples because the test signal is the output of a noisy linear time-invariant system, e.g., from channel propagation or filtering. We assume that the channel impulse response is unknown, but can be modeled as a random channel with finite first and second-order statistics that can be estimated from sample impulse responses. We present two kernels, the expected and projected RBF kernels, that account for the stochastic channel. Compared to the strategy of virtual examples, an SVM trained with the proposed kernels requires dramatically less training time, and may perform better in practice. We also extend the joint quadratic discriminant analysis (joint QDA) classifier, which also accounts for a stochastic channel, to a local version that reduces model bias. Results show the proposed methods achieve state-of-the-art performance and significantly faster training times.