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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Sergio Valcarcel Macua, Pavle Belanovic, Santiago Zazo
• Keywords: Feature Extraction Method, ICASSP 2011, Linear Discriminant Analysis, Realistic Distributed Classification, Signal Processing