NDLI: Perturbation regulated kernel regressors for supervised machine learning

Content Provider	IEEE Xplore Digital Library
Author	Kung, S.Y. Pei-yuan Wu
Copyright Year	2012
Description	Author affiliation: Princeton University, USA (Kung, S.Y.; Pei-yuan Wu)
Abstract	This paper develops a kernel perturbation-regulated (KPR) regressor based on the errors-in-variables models. KPR offers a strong smoothing capability critical to the robustness of regression or classification results. For Gaussian cases, the notion of orthogonal polynomials is instrumental to optimal estimation and its error analysis. More exactly, the regressor may be expressed as a linear combination of many simple Hermite Regressors, each focusing on one (and only one) orthogonal polynomial. For Gaussian or non-Gaussian cases, this paper formally establishes a “Two-Projection Theorem” allowing the estimation task to be divided into two projection stages: the first projection reveals the effect of model-induced error (caused by under-represented regressor models) while the second projection reveals the extra estimation error due to the (inevitable) input measuring error. The two-projection analysis leads to a closed-form error formula critical for order/error tradeoff. The simulation results not only confirm the theoretical prediction but also demonstrate superiority of KPR over the conventional ridge regression method in MSE reduction.
Starting Page	1
Ending Page	6
File Size	297956
Page Count	6
File Format	PDF
ISBN	9781467310246
ISSN	15512541
e-ISBN	9781467310260
e-ISBN	9781467310253
DOI	10.1109/MLSP.2012.6349743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Polynomials Kernel Vectors Robustness Regression analysis Smoothing methods Estimation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Time series filtering, smoothing and learning using the kernel Kalman filter

Semi-Supervised Kernel Methods for Regression Estimation

Biomedical video denoising using supervised manifold learning

The Dependent Error Regression Smoothing Approach For Semi-parametric Evoked Potential Estimation

Variance analyses for kernel regressors with nested reproducing kernel hilbert spaces

Active one-class learning by kernel density estimation

Semi-Supervised Kernel Regression

BYY data smoothing based learning on a small size of samples

Gaussian Kernel Width Optimization for Sparse Bayesian Learning

Perturbation regulated kernel regressors for supervised machine learning

Similar Documents

Time series filtering, smoothing and learning using the kernel Kalman filter

Semi-Supervised Kernel Methods for Regression Estimation

Biomedical video denoising using supervised manifold learning

The Dependent Error Regression Smoothing Approach For Semi-parametric Evoked Potential Estimation

Variance analyses for kernel regressors with nested reproducing kernel hilbert spaces

Active one-class learning by kernel density estimation

Semi-Supervised Kernel Regression

BYY data smoothing based learning on a small size of samples

Gaussian Kernel Width Optimization for Sparse Bayesian Learning

Perturbation regulated kernel regressors for supervised machine learning