Abstract
This paper proposes a novel solution for compressive polynomial regression learning. The solution comes in primal and dual closed-forms similar to that of ridge regression. Essentially, the proposed solution stretches the covariance computation by a power term thereby compresses or amplifies the estimation. Our experiments on both synthetic data and real-world data show effectiveness of the proposed method for compressive learning.
Original language | English |
---|---|
Title of host publication | 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 953-957 |
Number of pages | 5 |
ISBN (Electronic) | 9781479951994 |
DOIs | |
Publication status | Published - 2014 |
Event | 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore Duration: 2014 Dec 10 → 2014 Dec 12 |
Publication series
Name | 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 |
---|
Other
Other | 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 |
---|---|
Country/Territory | Singapore |
City | Singapore |
Period | 14/12/10 → 14/12/12 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Artificial Intelligence
- Control and Systems Engineering