Journal
INFORMATION SCIENCES
Volume 169, Issue 1-2, Pages 1-25Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2004.02.014
Keywords
least-squares; nearest neighbour; global-local learning; singular value decomposition; imputation
Categories
Ask authors/readers for more resources
Imputation of missing data is of interest in many areas such as survey data editing, medical documentation maintaining and DNA microarray data analysis. This paper is devoted to experimental analysis of a set of imputation methods developed within the so-called least-squares approximation approach, a non-parametric computationally effective multidimensional technique. First, we review global methods for least-squares data imputation. Then we propose extensions of these algorithms based on the nearest neighbours approach. An experimental study of the algorithms on generated data sets is conducted. It appears that straight algorithms may work rather well on data of simple structure and/or with small number of missing entries. However, in more complex cases, the only winner within the least-squares approximation approach is a method, INI, proposed in this paper as a combination of global and local imputation algorithms. (C) 2004 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available