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Abstract
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| 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. | |
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International
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Congress
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IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 |
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960 |
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Place
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Reviewers
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Si |
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ISBN/ISSN
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978-1-4577-0539-7 |
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Start Date
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22/05/2011 |
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End Date
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27/05/2011 |
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From page
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3288 |
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To page
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3291 |
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, |