Classification based on a permanental process with cyclic approximation

Jie Yang; Klaus Miescke; Peter McCullagh

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arXiv · 1108.4920

Classification based on a permanental process with cyclic approximation

Jie Yang·Klaus Miescke·Peter McCullagh

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Abstract

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.

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Jie Yang, Klaus Miescke, Peter McCullagh. 2011-08-24. Classification based on a permanental process with cyclic approximation. https://arxiv.org/abs/1108.4920

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