Embedded local feature selection within mixture of experts
We study the classification technique called mixture of experts. Mixture of experts has serious difficulties with high dimensional datasets. We derive an embedded feature selection within mixture of experts. Our technique overcomes the mixture of experts in terms of accuracy. Our technique recovers relevant according to experiments in artificial datasets.
local feature selection mixture of experts embedded feature selection regularization