Probabilistic Features on Simplex Manifold in Predictive Data Modelling

In this paper, we propose a probabilistic feature mapping technique for proportional data where the features are learned in an unsupervised manner. In computer vision, count data are very relevant and we can represent that as proportion which can be modeled with distributions for proportional data. We are interested in computing probabilistic features using scaled Dirichlet and shifted scaled Dirichlet distributions and map with discriminative features to improve classification accuracy. The parameters of these distributions is updated using maximum likelihood approach. Our experimental results show that the proposed feature mapping technique improves the accuracy of baseline kernels of support vector machines (SVM) for image classification tasks.