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Conference papers

Kernel Temporal Component Analysis (KTCA)

Dominique Martinez 1 Alistair Bray 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We describe an efficient algorithm for simultaneously extracting multiple smoothly-varying non-linear invariances from time-series data. The method exploits the concept of maximising temporal predictability introduced by Stone in the linear domain - we term this temporal component analysis (TCA). Our current work extends this linear method into the non-linear domain using kernel-based methods; it performs a non-linear projection of the input into an unknown high-dimensional feature space, computing a linear solution in this space. In this paper we describe the improved on-line version of this algorithm (KTCA) for working on very large data sets, and demonstrate its applicability for computer vision by extracting non-linear disparity directly from grey-level stereo pairs, without pre-processing.
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Submitted on : Tuesday, September 26, 2006 - 2:51:06 PM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM


  • HAL Id : inria-00100795, version 1



Dominique Martinez, Alistair Bray. Kernel Temporal Component Analysis (KTCA). European Symposium on Artificial Neural Networks - ESANN'2002, Apr 2002, Bruges, Belgium, pp.477-482. ⟨inria-00100795⟩



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