Online Clustering of Processes
Abstract
The problem of online clustering is considered in the case where each data point is a sequence generated by a stationary ergodic process. Data arrive in an online fashion so that the sample received at every time-step is either a continuation of some previously received sequence or a new sequence. The dependence between the sequences can be arbitrary. No parametric or independence assumptions are made; the only assumption is that the marginal distribution of each sequence is stationary and ergodic. A novel, computationally efficient algorithm is proposed and is shown to be asymptotically consistent (under a natural notion of consistency). The performance of the proposed algorithm is evaluated on simulated data, as well as on real datasets (motion classification).
Cite
Text
Khaleghi et al. "Online Clustering of Processes." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Khaleghi et al. "Online Clustering of Processes." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/khaleghi2012aistats-online/)BibTeX
@inproceedings{khaleghi2012aistats-online,
title = {{Online Clustering of Processes}},
author = {Khaleghi, Azadeh and Ryabko, Daniil and Mary, Jeremie and Preux, Philippe},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {601-609},
volume = {22},
url = {https://mlanthology.org/aistats/2012/khaleghi2012aistats-online/}
}