We analyse how the Generative Topographic Mapping (GTM) can be modified to cope with missing values in the training data. Our approach is based on an Expectation -Maximisation (EM) method which estimates the parameters of the mixture components and at the same time deals with the missing values. We incorporate this algorithm into a hierarchical GTM. We verify the method on a toy data set (using a single GTM) and a realistic data set (using a hierarchical GTM). The results show our algorithm can help to construct informative visualisation plots, even when some of the training points are corrupted with missing values.
|Place of Publication||Birmingham, UK|
|Number of pages||9|
|Publication status||Unpublished - 2001|
- Generative Topographic Mapping (GTM)
- missing values
- Expectation -Maximisation (EM)
- visualisation plots