Using Stochastic Encoders to Discover Structure in Data
| dc.creator | Luttrell, Stephen | |
| dc.date | 2004-08-21 | |
| dc.date.accessioned | 2026-07-07T03:21:42Z | |
| dc.date.available | 2026-07-07T03:21:42Z | |
| dc.description | In this paper a stochastic generalisation of the standard Linde-Buzo-Gray (LBG) approach to vector quantiser (VQ) design is presented, in which the encoder is implemented as the sampling of a vector of code indices from a probability distribution derived from the input vector, and the decoder is implemented as a superposition of reconstruction vectors. This stochastic VQ (SVQ) is optimised using a minimum mean Euclidean reconstruction distortion criterion, as in the LBG case. Numerical simulations are used to demonstrate how this leads to self-organisation of the SVQ, where different stochastically sampled code indices become associated with different input subspaces. | |
| dc.description | 18 pages, 9 figures. Full version of a short paper that was published in the Digest of the 5th IMA International Conference on Mathematics in Signal Processing, 18-20 December 2000, Warwick University, UK | |
| dc.identifier | https://arxiv.org/abs/cs/0408049 | |
| dc.identifier | http://arxiv.org/abs/cs/0408049 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32304 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.subject | Computer Vision and Pattern Recognition | |
| dc.subject | I.2.6; I.5.1 | |
| dc.title | Using Stochastic Encoders to Discover Structure in Data | |
| dc.type | text |