Online Discriminative Kernel Density Estimator With Gaussian Kernels

We propose a new method for a supervised online estimation of probabilistic discriminative models for classification tasks. The method estimates the class distributions from a stream of data in the form of Gaussian mixture models (GMMs). The reconstructive updates of the distributions are based on the recently proposed online kernel density estimator (oKDE). We maintain the number of components in the model low by compressing the GMMs from time to time. We propose a new cost function that measures loss of interclass discrimination during compression, thus guiding the compression toward simpler models that still retain discriminative properties. The resulting classifier thus independently updates the GMM of each class, but these GMMs interact during their compression through the proposed cost function. We call the proposed method the online discriminative kernel density estimator (odKDE). We compare the odKDE to oKDE, batch state-of-the-art kernel density estimators (KDEs), and batch/incremental support vector machines (SVM) on the publicly available datasets. The odKDE achieves comparable classification performance to that of best batch KDEs and SVM, while allowing online adaptation from large datasets, and produces models of lower complexity than the oKDE.