Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model
Abstract
Activity recognition from sensor data has spurred a great deal of interest due to its impact on health care. Prior work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort to produce training data with the start and end times of each activity. In order to reduce the annotation effort, we present a weakly supervised approach based on multi-instance learning. We introduce a generative graphical model for multi-instance learning on time series data based on an auto-regressive hidden Markov model. Our model has a number of advantages, including the ability to produce both bag and instance-level predictions as well as an efficient exact inference algorithm based on dynamic programming.
Cite
Text
Guan et al. "Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model." International Conference on Machine Learning, 2016.Markdown
[Guan et al. "Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/guan2016icml-efficient/)BibTeX
@inproceedings{guan2016icml-efficient,
title = {{Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model}},
author = {Guan, Xinze and Raich, Raviv and Wong, Weng-Keen},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {2330-2339},
volume = {48},
url = {https://mlanthology.org/icml/2016/guan2016icml-efficient/}
}