Given a n-dimensional sample composed of a mixture of m subsamples with different probability density functions (p.d.f.), it is possible to build a (m - 1)-dimensional distribution that carries all the information about the subsample proportions in the mixture sample. This projection can be estimated without an analytical knowledge of the p.d.f.'s of the different subsamples with the aid, for instance, of neural networks. This way, if m - 1 < n it is possible to estimate the proportions of the mixture sample in a lower (m - 1)-dimensional space without losing sensitivity. © 1997 Elsevier Science B.V.
|Journal||Computer Physics Communications|
|Publication status||Published - 1 Aug 1997|