Abstract
The present work introduces a simple scheme for active classification of data using unsupervised ensembles of classifiers. Uncertainty sampling, with different uncertainty measures, is evaluated for data selection, while an online expectation maximization algorithm is derived to estimate model parameters on-the-fly. Preliminary tests on real data showcase the potential of the novel approach.
| Original language | English (US) |
|---|---|
| Title of host publication | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3967-3971 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509066315 |
| DOIs | |
| State | Published - May 2020 |
| Event | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: May 4 2020 → May 8 2020 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 2020-May |
| ISSN (Print) | 1520-6149 |
Conference
| Conference | 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 |
|---|---|
| Country/Territory | Spain |
| City | Barcelona |
| Period | 5/4/20 → 5/8/20 |
Bibliographical note
Funding Information:Work in this paper was supported in part by NSF grants 1514056, 1500713, 1711471, and 1901134. Emails: traga003@umn.edu, dbermper@andrew.cmu.edu, georgios@umn.edu
Publisher Copyright:
© 2020 IEEE.
Keywords
- Unsupervised ensemble classification
- active learning
- ensemble learning