GTM: the generative topographic mapping
Neural Computation
Manifold constrained finite gaussian mixtures
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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The diagnosis and prognosis of human brain tumours, especially when they are aggresive, are sensitive clinical tasks that usually require non-invasive measurement techniques. Outcome information for aggressive tumours, in particular, is usually scarce. In this paper, we aim to gauge the capability of a novel semi-supervised model, SS-Geo-GTM, to infer outcome stages from a very limited amount of available stage labels and Magnetic Resonance Spectroscopy (MRS) data corresponding to Glioblastoma, which is an aggressive tumor type. This model stems from a geodesic distance-based extension of Generative Topographic Mapping (Geo-GTM) that prioritizes neighbourhood relationships along a generated manifold embedded in the observed data space.