Making sense of pervasive signals: a machine learning approach

Abstract

This study focused on challenges come from noisy and complex pervasive data. We proposed new Bayesian nonparametric models to infer co-patterns from multi-channel data collected from pervasive devices. By making sense of pervasive data, the study contributes to the development of Machine Learning and Data Mining in Big Data era