Online variational inference on finite multivariate Beta mixture models for medical applications

Abstract

Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi‐class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria.

Human Activity Recognition with an HMM-Based Generative Model

“…VI is an approximation technique, more accurate compared to ML and faster than fully Bayesian inference [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 ]. Moreover, compared to a deterministic method, such as maximum likelihood, it does not suffer from convergence to a local maximum and over-fitting.…”