### Abstract

Original language | English |
---|---|

Place of Publication | Birmingham |

Publisher | Aston University |

Number of pages | 26 |

ISBN (Print) | NCRG/2007/04 |

Publication status | Published - 12 Oct 2007 |

### Fingerprint

### Keywords

- multivariate statistics
- generative topographic mapping deals with missing data

### Cite this

*Data visualisation with missing data: A non-linear approach*. Birmingham: Aston University.

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**Data visualisation with missing data: A non-linear approach.** / Schroeder, Martin; Cornford, Dan.

Research output: Working paper › Technical report

TY - UNPB

T1 - Data visualisation with missing data: A non-linear approach

AU - Schroeder, Martin

AU - Cornford, Dan

PY - 2007/10/12

Y1 - 2007/10/12

N2 - 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.

AB - 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.

KW - multivariate statistics

KW - generative topographic mapping deals with missing data

M3 - Technical report

SN - NCRG/2007/04

BT - Data visualisation with missing data: A non-linear approach

PB - Aston University

CY - Birmingham

ER -