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Communication Dans Un Congrès Année : 2022

Functional mixture-of-experts for classification

Résumé

We develop a mixtures-of-experts (ME) approach to the multiclass classification where the predictors are univariate functions. It consists of a ME model in which both the gating network and the experts network are constructed upon multinomial logistic activation functions with functional inputs. We perform a regularized maximum likelihood estimation in which the coefficient functions enjoy interpretable sparsity constraints on targeted derivatives. We develop an EM-Lasso like algorithm to compute the regularized MLE and evaluate the proposed approach on simulated and real data.

Dates et versions

hal-03598266 , version 1 (04-03-2022)

Identifiants

Citer

Nhat Thien Pham, Faicel Chamroukhi. Functional mixture-of-experts for classification. 53èmes Journées de Statistique de la Société Française de Statistique (SFdS), Jun 2022, Lyon, France. ⟨hal-03598266⟩
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