Missing value imputation: with application to handwriting data

Zhen Xu; Sargur N. Srihari

doi:10.1117/12.2075842

8 February 2015 Missing value imputation: with application to handwriting data

Zhen Xu, Sargur N. Srihari

Proceedings Volume 9402, Document Recognition and Retrieval XXII; 94020P (2015) https://doi.org/10.1117/12.2075842
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

Missing values make pattern analysis difficult, particularly with limited available data. In longitudinal research, missing values accumulate, thereby aggravating the problem. Here we consider how to deal with temporal data with missing values in handwriting analysis. In the task of studying development of individuality of handwriting, we encountered the fact that feature values are missing for several individuals at several time instances. Six algorithms, i.e., random imputation, mean imputation, most likely independent value imputation, and three methods based on Bayesian network (static Bayesian network, parameter EM, and structural EM), are compared with children's handwriting data. We evaluate the accuracy and robustness of the algorithms under different ratios of missing data and missing values, and useful conclusions are given. Specifically, static Bayesian network is used for our data which contain around 5% missing data to provide adequate accuracy and low computational cost.

Citation Download Citation

Zhen Xu and Sargur N. Srihari "Missing value imputation: with application to handwriting data", Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020P (8 February 2015); https://doi.org/10.1117/12.2075842

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