  The transcriptomics of cancer tumors are characterized with tens of thousands
of gene expression features. Patient prognosis or tumor stage can be assessed
by machine learning techniques like supervised classification tasks given a
gene expression profile. Feature selection is a useful approach to select the
key genes which helps to classify tumors. In this work we propose a feature
selection method based on Multiple Kernel Learning that results in a reduced
subset of genes and a custom kernel that improves the classification
performance when used in support vector classification. During the feature
selection process this method performs a novel latent regularisation by
relaxing the supervised target problem by introducing unsupervised structure
obtained from the latent space learned by a non linear dimensionality reduction
model. An improvement of the generalization capacity is obtained and assessed
by the tumor classification performance on new unseen test samples when the
classifier is trained with the features selected by the proposed method in
comparison with other supervised feature selection approaches.
