Comparative assessment of the robustness of missing data imputation through generative topographic mapping

Authors:
Iván Olier;Alfredo Vellido
Affiliations:
Department of Computing Languages and Systems (LSI), Polytechnic University of Catalonia (UPC), Barcelona, Spain;Department of Computing Languages and Systems (LSI), Polytechnic University of Catalonia (UPC), Barcelona, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 8
Cited 5

A practical Bayesian framework for backpropagation networks

Neural Computation
GTM: the generative topographic mapping

Neural Computation
Robust automatic speech recognition with missing and unreliable acoustic data

Speech Communication
Self-Organizing Maps

Self-Organizing Maps
Mixture model clustering for mixed data with missing information

Computational Statistics & Data Analysis
Learning from Incomplete Data

Learning from Incomplete Data
Robust mixture modelling using multivariate t-distribution with missing information

Pattern Recognition Letters
Selective smoothing of the generative topographic mapping

IEEE Transactions on Neural Networks

Exploring the ecological status of human altered streams through Generative Topographic Mapping

Environmental Modelling & Software
Advances in clustering and visualization of time series using GTM through time

Neural Networks
Robust analysis of MRS brain tumour data using t-GTM

Neurocomputing
Missing data imputation: a fuzzy K-means clustering algorithm over sliding window

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A variational Bayesian approach for the robust analysis of the cortical silent period from EMG recordings of brain stroke patients

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The incompleteness of data is a most common source of uncertainty in real-world Data Mining applications. The management of this uncertainty is, therefore, a task of paramount importance for the data analyst. Many methods have been developed for missing data imputation, but few of them approach the problem of imputation as part of a general data density estimation scheme. Amongst the latter, a method for imputing and visualizing multivariate missing data using Generative Topographic Mapping was recently presented. This model and some of its extensions are tested under different experimental conditions. Its performance is compared to that of other missing data imputation techniques, thus assessing its relative capabilities and limitations.