Self-organization and missing values in SOM and GTM
Research output: Contribution to journal › Article › Scientific › peer-review
Researchers
Research units
- VTT Technical Research Centre of Finland
- Steno Diabetes Center
Abstract
In this paper, we study fundamental properties of the Self-Organizing Map (SOM) and the Generative Topographic Mapping (GTM), ramifications of the initialization of the algorithms and properties of the algorithms in the presence of missing data. We show that the commonly used principal component analysis (PCA) initialization of the GTM does not guarantee good learning results with high-dimensional data. Initializing the GTM with the SOM is shown to yield improvements in self-organization with three high-dimensional data sets: commonly used MNIST and ISOLET data sets and epigenomic ENCODE data set. We also propose a revision of handling missing data to the batch SOM algorithm called the Imputation SOM and show that the new algorithm is more robust in the presence of missing data. We benchmark the performance of the topographic mappings in the missing value imputation task and conclude that there are better methods for this particular task. Finally, we announce a revised version of the SOM Toolbox for Matlab with added GTM functionality.
Details
Original language | English |
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Pages (from-to) | 60-70 |
Number of pages | 11 |
Journal | Neurocomputing |
Volume | 147 |
Issue number | 1 |
Publication status | Published - 5 Jan 2015 |
MoE publication type | A1 Journal article-refereed |
- Data visualization, Generative topographic mapping, Missing data, Self-organization, Self-organizing map
Research areas
ID: 2025441