- Deep Belief Networks for dimensionality reduction
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
Deep Belief Networks are probabilistic generative models which are composed by multiple layers of latent stochastic variables. The top two layers have symmetric undirected connections, while the lower layers receive directed top-down connections from the layer above. The current state-of-the-art training method for DBNs is contrastive divergence, an efficient learning technique that can approximate and follow the gradient of the data likelihood with respect to the model parameters. In this work we explore the quality of the non-linear dimensionality reduction achieved through a DBN on face images. We compare the results achieved to the well know Principal Component Analysis as well as with a Harmonium model, which is the top layer of a DBN.
- Proceedings title: BNAIC 2008: Belgian-Dutch Conference on Artificial Intelligence: proceedings of the twentieth Belgian-Dutch
Conference on Artificial Intelligence: Enschede, October 30-31, 2008
Publisher: University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science
Place of publication: Enschede
Editors: A. Nijholt, M. Pantic, M. Poel, H. Hondorp
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