A Neural Network For L1 Norm Linear Regression

James P. Helferty; James A. Stover; John J. Helferty

doi:10.1117/12.969918

1 February 1990 A Neural Network For L₁ Norm Linear Regression

James P. Helferty, James A. Stover, John J. Helferty

Proceedings Volume 1196, Intelligent Control and Adaptive Systems; (1990) https://doi.org/10.1117/12.969918
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

We are presenting a neural network optimization circuit for robust regression. The minimization of Least Square Error (LSE), L2 norm, cost functions is predominantly used for fitting functions to data points, but LSE approaches are highly sensitive to outlier points, or points that don't follow the trend. To alleviate this problem, we replace the L2 norm error function by an L1 norm error function which sums the absolute deviation of the errors and puts less weight on outlier points. An analog neural network optimization circuit is then developed to minimize the sum of the L1 norm error function. Simulation examples are presented on example data sets that compare the neural network solution with LSE solution .

Citation Download Citation

James P. Helferty, James A. Stover, and John J. Helferty "A Neural Network For L₁ Norm Linear Regression", Proc. SPIE 1196, Intelligent Control and Adaptive Systems, (1 February 1990); https://doi.org/10.1117/12.969918

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available