Comparison With Kohonen's Self-organizing Maps
While nodes in the self-organizing map (SOM) can wander around at will, GTM nodes are constrained by the allowable transformations and their probabilities. If the deformations are well-behaved the topology of the latent space is preserved.
The SOM was created as a biological model of neurons and is a heuristic algorithm. By contrast, the GTM has nothing to do with neuroscience or cognition and is a probabilistically principled model. Thus, it has a number of advantages over SOM, namely:
- it explicitly formulates a density model over the data.
- it uses a cost function that quantifies how well the map is trained.
- it uses a sound optimization procedure (EM algorithm).
GTM was introduced by Bishop, Svensen and Williams in their Technical Report in 1997 (Technical Report NCRG/96/015, Aston University, UK) published later in Neural Computation. It was also described in the PhD thesis of Markus Svensen (Aston, 1998).
Read more about this topic: Generative Topographic Map
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