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Classification based on a permanental process with cyclic approximation. (English) Zbl 1452.62476

Summary: 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 nonreducible high-dimensional features.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
60G55 Point processes (e.g., Poisson, Cox, Hawkes processes)
62P10 Applications of statistics to biology and medical sciences; meta analysis
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