Online Discriminative Kernel Density Estimation

We propose a new method for online estimation of probabilistic discriminative models. The method is based on the recently proposed online Kernel Density Estimation \mbox(oKDE) framework which produces Gaussian mixture models and allows adaptation using only a single data point at a time. The oKDE builds reconstructive models from the data, and we extend it to take into account the interclass discrimination through a new distance function between the classifiers. We arrive at an online discriminative Kernel Density Estimators \mbox(odKDE). We compare the odKDE to oKDE, batch state-of-the-art KDEs and support vector machine (SVM) on a standard database. The odKDE achieves comparable classification performance to that of best batch KDEs and SVM, while allowing online adaptation, and produces models of lower complexity than the oKDE.