Statistical analysis with missing data
Statistical analysis with missing data
GTM: the generative topographic mapping
Neural Computation
Deterministic annealing EM algorithm
Neural Networks
Mixtures of probabilistic principal component analyzers
Neural Computation
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust mixture modelling using the t distribution
Statistics and Computing
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Finite mixture regression model with random effects: application to neonatal hospital length of stay
Computational Statistics & Data Analysis
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Learning from Incomplete Data
Robust mixture modelling using multivariate t-distribution with missing information
Pattern Recognition Letters
Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm
Data Mining and Knowledge Discovery
Outlier detection in scatterometer data: neural network approaches
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
High breakdown mixture discriminant analysis
Journal of Multivariate Analysis
SMEM Algorithm for Mixture Models
Neural Computation
Robust Bayesian mixture modelling
Neurocomputing
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
Selective smoothing of the generative topographic mapping
IEEE Transactions on Neural Networks
On the Initialization of Two-Stage Clustering with Class-GTM
Current Topics in Artificial Intelligence
Unfolding the Manifold in Generative Topographic Mapping
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Geodesic Generative Topographic Mapping
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
On the Improvement of the Mapping Trustworthiness and Continuity of a Manifold Learning Model
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
On the improvement of brain tumour data clustering using class information
Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
Multivariate Student-t self-organizing maps
Neural Networks
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Neural networks and other machine learning methods in cancer research
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
On the influence of class information in the two-stage clustering of a human brain tumour dataset
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Probabilistic self-organizing maps for qualitative data
Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
Cartogram visualization for nonlinear manifold learning models
Data Mining and Knowledge Discovery
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The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural network-inspired, Self-Organizing Maps. The GTM can also be interpreted as a constrained mixture of distribution models. In recent years, much attention has been directed towards Student t-distributions as an alternative to Gaussians in mixture models due to their robustness towards outliers. In this paper, the GTM is redefined as a constrained mixture of t-distributions: the t-GTM, and the Expectation-Maximization algorithm that is used to fit the model to the data is modified to carry out missing data imputation. Several experiments show that the t-GTM successfully detects outliers, while minimizing their impact on the estimation of the model parameters. It is also shown that the t-GTM provides an overall more accurate imputation of missing values than the standard Gaussian GTM.