Twisting key absolute space for stretchy polynomial regression

Kar Ann Toh

doi:10.1109/ICARCV.2014.7064434

Twisting key absolute space for stretchy polynomial regression

Kar Ann Toh

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 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	https://doi.org/10.1109/ICARCV.2014.7064434
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

Access to Document

10.1109/ICARCV.2014.7064434

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

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title = "Twisting key absolute space for stretchy polynomial regression",

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.",

author = "Toh, {Kar Ann}",

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doi = "10.1109/ICARCV.2014.7064434",

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series = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014",

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Toh, KA 2014, Twisting key absolute space for stretchy polynomial regression. in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014., 7064434, 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, Institute of Electrical and Electronics Engineers Inc., pp. 953-957, 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, Singapore, Singapore, 14/12/10. https://doi.org/10.1109/ICARCV.2014.7064434

Twisting key absolute space for stretchy polynomial regression. / Toh, Kar Ann.

2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 953-957 7064434 (2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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

Twisting key absolute space for stretchy polynomial regression

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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Cite this