Topological mappings of video and audio data

Colin Fyfe, Wesam Barbakh, Wei Chuan Ooi, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)481-489
Number of pages9
JournalInternational Journal of Neural Systems
Issue number6
Publication statusPublished - 2008 Dec

ASJC Scopus subject areas

  • Computer Networks and Communications


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