# Random projection RBF nets for multidimensional density estimation

International Journal of Applied Mathematics and Computer Science (2008)

- Volume: 18, Issue: 4, page 455-464
- ISSN: 1641-876X

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topEwa Skubalska-Rafajłowicz. "Random projection RBF nets for multidimensional density estimation." International Journal of Applied Mathematics and Computer Science 18.4 (2008): 455-464. <http://eudml.org/doc/207899>.

@article{EwaSkubalska2008,

abstract = {The dimensionality and the amount of data that need to be processed when intensive data streams are observed grow rapidly together with the development of sensors arrays, CCD and CMOS cameras and other devices. The aim of this paper is to propose an approach to dimensionality reduction as a first stage of training RBF nets. As a vehicle for presenting the ideas, the problem of estimating multivariate probability densities is chosen. The linear projection method is briefly surveyed. Using random projections as the first (additional) layer, we are able to reduce the dimensionality of input data. Bounds on the accuracy of RBF nets equipped with a random projection layer in comparison to RBF nets without dimensionality reduction are established. Finally, the results of simulations concerning multidimensional density estimation are briefly reported.},

author = {Ewa Skubalska-Rafajłowicz},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {radial basis functions; multivariate density estimation; dimension reduction; normal random projection; novelty detection},

language = {eng},

number = {4},

pages = {455-464},

title = {Random projection RBF nets for multidimensional density estimation},

url = {http://eudml.org/doc/207899},

volume = {18},

year = {2008},

}

TY - JOUR

AU - Ewa Skubalska-Rafajłowicz

TI - Random projection RBF nets for multidimensional density estimation

JO - International Journal of Applied Mathematics and Computer Science

PY - 2008

VL - 18

IS - 4

SP - 455

EP - 464

AB - The dimensionality and the amount of data that need to be processed when intensive data streams are observed grow rapidly together with the development of sensors arrays, CCD and CMOS cameras and other devices. The aim of this paper is to propose an approach to dimensionality reduction as a first stage of training RBF nets. As a vehicle for presenting the ideas, the problem of estimating multivariate probability densities is chosen. The linear projection method is briefly surveyed. Using random projections as the first (additional) layer, we are able to reduce the dimensionality of input data. Bounds on the accuracy of RBF nets equipped with a random projection layer in comparison to RBF nets without dimensionality reduction are established. Finally, the results of simulations concerning multidimensional density estimation are briefly reported.

LA - eng

KW - radial basis functions; multivariate density estimation; dimension reduction; normal random projection; novelty detection

UR - http://eudml.org/doc/207899

ER -

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