Twinned Topographic Maps for Decision Making in the Cockpit
There is consensus amongst aviation researchers and practitioner that some 70% of all aircraft accidents have human error as a root cause . Thatcher, Fyfe and Jain  have suggested an intelligent landing support system, comprising of three agents, that will support the flight crew in the most critical phase of a flight, the approach and landing. The third agent is envisaged to act as a pattern matching agent or an ‘extra pilot’ in the cockpit to aid decision making. This paper will review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) . But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts . We show visualisation results on some real and artificial data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data.
KeywordsLatent Point Topographic Mapping Data Space Latent Variable Model Harmonic Average
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