Online Program

Classification based on a permanental process with application to microarray analysis

Peter McCullagh, University of Chicago 
Klaus Miescke, University of Illinois at Chicago 
*Jie Yang, University of Illinois at Chicago 

Keywords: Cyclic approximation, DNA microarray analysis, High-dimensional data, Supervised classification, Weighted permanental ratio

We introduce a doubly stochastic marked point process model for supervised classification problems. Regardless of the number of classes or the dimension of the feature space, the model requires only 2–3 parameters for the covariance function. The classification criterion involves a permanental ratio for which an approximation using a polynomial-time cyclic expansion is proposed. The approximation is effective even if the feature region occupied by one class is a patchwork interlaced with regions occupied by other classes. An application to DNA microarray analysis indicates that the cyclic approximation is effective even for high-dimensional data. It can employ feature variables in an efficient way to reduce the prediction error significantly. This is critical when the true classification relies on non-reducible high-dimensional features.