科研成果详情

Clustering is an attractive method to handle large-scale data which are explosively generated through digitization. This approach is specifically appropriate when labeling is very costly. In this paper, we constructed an unsupervised learning algorithm and focused on a finite mixture model based on multivariate Beta distribution. Our motivation is the flexibility and high potential that this distribution offers in modeling data. To learn this mixture model, we used an expectation propagation inference framework in which the parameters and the complexity of the model were evaluated concurrently in a single optimization framework. We evaluated the performance of our framework on publicly available datasets related to forgery detection, EEG-based sentiment analysis and human activity recognition. Our proposed model demonstrates comparable results to similar alternatives.