Data visualisation with missing data: A non-linear approach

Schroeder, Martin and Cornford, Dan (2007). Data visualisation with missing data: A non-linear approach. Technical Report. Aston University, Birmingham.

Abstract

Exploratory analysis of data in all sciences seeks to find common patterns to gain insights into the structure and distribution of the data. Typically visualisation methods like principal components analysis are used but these methods are not easily able to deal with missing data nor can they capture non-linear structure in the data. One approach to discovering complex, non-linear structure in the data is through the use of linked plots, or brushing, while ignoring the missing data. In this technical report we discuss a complementary approach based on a non-linear probabilistic model. The generative topographic mapping enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate far more structure than a two dimensional principal components plot could, and deal at the same time with missing data. We show that using the generative topographic mapping provides us with an optimal method to explore the data while being able to replace missing values in a dataset, particularly where a large proportion of the data is missing.

Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Computer science research group
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Aston University (General)
Uncontrolled Keywords: multivariate statistics,generative topographic mapping deals with missing data
Published Date: 2007-10-12
Authors: Schroeder, Martin
Cornford, Dan ( 0000-0001-8787-6758)

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