Twisting key absolute space for stretchy polynomial regression

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages953-957
Number of pages5
ISBN (Electronic)9781479951994
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: 2014 Dec 102014 Dec 12

Publication series

Name2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014

Other

Other2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
CountrySingapore
CitySingapore
Period14/12/1014/12/12

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Artificial Intelligence
  • Control and Systems Engineering

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  • Cite this

    Toh, K. A. (2014). Twisting key absolute space for stretchy polynomial regression. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 (pp. 953-957). [7064434] (2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARCV.2014.7064434