IEEE Trans Neural Netw Learn Syst - Sparse multivariate gaussian mixture regression.

Tópicos

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Resumo

Fitting a multivariate Gaussian mixture to data represents an attractive, as well as challenging problem, in especial when sparsity in the solution is demanded. Achieving this objective requires the concurrent update of all parameters (weight, centers, and precisions) of all multivariate Gaussian functions during the learning process. Such is the focus of this paper, which presents a novel method founded on the minimization of the error of the generalized logarithmic utility function (GLUF). This choice, which allows us to move smoothly from the mean square error (MSE) criterion to the one based on the logarithmic error, yields an optimization problem that resembles a locally convex problem and can be solved with a quasi-Newton method. The GLUF framework also facilitates the comparative study between both extremes, concluding that the classical MSE optimization is not the most adequate for the task. The performance of the proposed novel technique is demonstrated on simulated as well as realistic scenarios.

Resumo Limpo

fit multivari gaussian mixtur data repres attract well challeng problem especi sparsiti solut demand achiev object requir concurr updat paramet weight center precis multivari gaussian function learn process focus paper present novel method found minim error general logarithm util function gluf choic allow us move smooth mean squar error mse criterion one base logarithm error yield optim problem resembl local convex problem can solv quasinewton method gluf framework also facilit compar studi extrem conclud classic mse optim adequ task perform propos novel techniqu demonstr simul well realist scenario

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