Abstract
Learning in the presence of missing data is a pervasive problem in statistical data analysis. This paper deals with tracking the dynamic charging behavior of electric vehicle consumers, when some of the consumers' consumption decisions are missing. The problem is then formulated as an online classification task with missing labels. An online algorithm is proposed to jointly impute the missing data while at the same time learn from the complete data using an online convex optimization approach.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 243-247 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479970889 |
| DOIs | |
| State | Published - Feb 5 2014 |
| Event | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States Duration: Dec 3 2014 → Dec 5 2014 |
Publication series
| Name | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 |
|---|
Other
| Other | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 |
|---|---|
| Country | United States |
| City | Atlanta |
| Period | 12/3/14 → 12/5/14 |
Keywords
- Conditional random field
- Misses
- Online convex optimization
- Smart grid
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Cite this
Soltani, N. Y., & Giannakis, G. B. (2014). Online learning of electric vehicle consumers' charging behavior with missing data. In 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 (pp. 243-247). [7032115] (2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2014.7032115