A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines

Restricted Boltzmann machines (RBMs) are energy-based neural-networks which are commonly used as the building blocks for deep architectures neural architectures. In this work, we derive a deterministic framework for the training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer (TAP) mean-field approximation of widely-connected systems with weak interactions coming from spin-glass theory. While the TAP approach has been extensively studied for fully-visible binary spin systems, our construction is generalized to latent-variable models, as well as to arbitrarily distributed real-valued spin systems with bounded support. In our numerical experiments, we demonstrate the effective deterministic training of our proposed models and are able to show interesting features of unsupervised learning which could not be directly observed with sampling. Additionally, we demonstrate how to utilize our TAP-based framework for leveraging trained RBMs as joint priors in denoising problems

Verification approach for medical data in e-healthcare system based on biometric and watermarking

Medical information is crucial in the healthcare system, and its manipulation can lead to misdiagnosis. Medical images also contain personal information for patients; hence, information security and privacy protection are paramount when transferring medical images over the Internet. Biometric approach and watermarking techniques are used to achieve this purpose. The focus of this paper was on a biometric watermarking system with a frequency domain in which the sender's iris code is employed as a sender authentication key. The privacy of the patient's information is preserved by encrypting it and embedding the key in the cover medical image created by the Discrete Wavelet Transform. The algorithm has shown that the proposed system has met previous requirements