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http://hdl.handle.net/20.500.12358/24806
Title | Topological mappings of video and audio data |
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Untitled | |
Abstract |
We 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).1 But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts.2 We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels. Finally we note that we may dispense with the probabilistic underpinnings of the product of experts and derive the same algorithm as a minimisation of mean squared error between the prototypes and the data. This leads us to suggest a new algorithm which incorporates local and global information in the clustering. Both ot the new algorithms achieve better results than the standard Self-Organizing Map. |
Type | Journal Article |
Date | 2008 |
Published in | International Journal of Neural Systems |
Series | Volume: 18, Number: 06 |
Publisher | World Scientific Publishing Company |
Citation | |
Item link | Item Link |
License | ![]() |
Collections | |
Files in this item | ||
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Ashour, Wesam M._17.pdf | 287.5Kb |