Expression Microarray Data Classification Using Counting Grids and Fisher Kernel

Hybrid generative-discriminative models are useful in biomedical applications- generative modeling extracts interpretable features from raw data, highlighting its properties and increasing classification accuracy when used as input for a discriminative classifier. This raises the question: which generative model should be used for a particular application? In this paper we apply a recently proposed generative model called the Counting Grid to expression microarray data and derive the corresponding Fisher kernel. We justify why this model is particularly well-suited for this application and evaluate classification accuracy on four gene expression data sets, including three tumor data sets and a blood sample data set from schizophrenic patients and healthy controls. We report state of the art results on three of the analyzed data sets and closely match the accuracy from previous work on the other.