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ICASSP
2011
IEEE

Distributed linear discriminant analysis

9 years 2 months ago
Distributed linear discriminant analysis
Linear discriminant analysis (LDA) is a widely used feature extraction method for classification. We introduce distributed implementations of different versions of LDA, suitable for many real applications. Classical eigen-formulation, iterative optimization of the subspace, and regularized LDA can be asymptotically approximated by all the nodes through local computations and single-hop communications among neighbors. These methods are based on the computation of the scatter matrices, so we introduce how to estimate them in a distributed fashion. We test the algorithms in a realistic distributed classification problem, achieving a performance near to the centralized solution and a significant improvement of 35% over the non-cooperative case.
Sergio Valcarcel Macua, Pavle Belanovic, Santiago
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Sergio Valcarcel Macua, Pavle Belanovic, Santiago Zazo
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