A Localized MKL Method for Brain Classification with Known Intra-class Variability

Automatic decisional systems based on pattern classification methods are becoming very important to support medical diagnosis. In general, the overall objective is to classify between healthy subjects and patients affected by a certain disease. To reach this aim, significant efforts have been spent in finding reliable biomarkers which are able to robustly discriminate between the two populations (i.e., patients and controls). However, in real medical scenarios there are many factors, like the gender or the age, which make the source data very heterogeneous. This introduces a large intra-class variation by affecting the performance of the classification procedure. In this paper we exploit how to use the knowledge on heterogeneity factors to improve the classification accuracy. We propose a Clustered Localized Multiple Kernel Learning (CLMKL) algorithm by encoding in the classication model the information on the clusters of apriory known stratifications. Experiments are carried out for brain classification in Schizophrenia. We show that our algorithm performs clearly better than single kernel Support Vector Machines (SVMs), linear MKL algorithms and canonical Localized MKL algorithms when the gender information is considered as apriori knowledge.