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TOPOLOGICAL MAPPINGS OF VIDEO AND AUDIO DATA

COLIN FYFE

Applied Computational Intelligence Research Unit, The University of The West of Scotland, UK

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WESAM BARBAKH

Applied Computational Intelligence Research Unit, The University of The West of Scotland, UK

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WEI CHUAN OOI

Department of Electronics and Computer Engineering, Korea University, Korea

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, and

HANSEOK KO

Department of Electronics and Computer Engineering, Korea University, Korea

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https://doi.org/10.1142/S0129065708001749Cited by:11 (Source: Crossref)

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.

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