Descripción
|
|
---|---|
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. | |
Internacional
|
Si |
Nombre congreso
|
IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 |
Tipo de participación
|
960 |
Lugar del congreso
|
|
Revisores
|
Si |
ISBN o ISSN
|
978-1-4577-0539-7 |
DOI
|
|
Fecha inicio congreso
|
22/05/2011 |
Fecha fin congreso
|
27/05/2011 |
Desde la página
|
3288 |
Hasta la página
|
3291 |
Título de las actas
|
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, |