Nearest neighbour approach in the least-squares data imputation algorithms
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.
nearest neighbour least-squares imputation globallocal learning singular value decomposition