Application of Classification Restricted Boltzmann Machine with discriminative and sparse learning to medical domains

Recent developments have demonstrated deep models to be very powerful generative models which are able to extract features automatically and obtain high predictive performance. Typically, a building block of a deep architecture is Restricted Boltzmann Machine (RBM). In this work, we focus on a variant of RBM adopted to the classification setting, which is known as Classification Restricted Boltzmann Machine. We claim that this model should be used as a stand-alone non-linear classifier which could be extremely useful in medical domains. Additionally, we show how to obtain sparse representation in RBM by adding a regularization term to the learning objective which enforces sparse solution. The considered classifier is then applied to five different medical domains. Keywords—Restricted Boltzmann Machine, Classification, Sparse, Medical domain, Diabetes, Oncology, Thyroid.