Generative classification model for categorical data based on latent Gaussian process

Abstract

In many machine learning applications such as computer-aided diagnosis, gene sequence analysis or natural language processing, categorical data appears. For small-scale data set with high dimensions, since relatively small proportion of possible categorical configurations are covered by training samples, conventional methods based on frequency information such as Dirichlet Compound Multinomial distribution usually runs into problems of over-fitting. Latent gaussian process is an effective bayesian non-parametric technique for categorical data modeling, which was proposed as an unsupervised method to embed unlabelled categorical data into a continuous and low-dimensional space through gaussian process. As a probabilistic generative model, latent gaussian process owns the ability of density estimation. In this paper, we propose a generative classification model as a supervised method for labelled categorical data, in which we use latent gaussian process to estimate the class-conditional densities. Since the complexity of gaussian process model can adapt to the size of training data, our method is able to effectively model small-sale categorical data. Experimental results show that our proposal can achieve better classification performance compared with other classification models for categorical data.