# A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

@article{Nguyen2022ANA, title={A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models}, author={TrungTin Nguyen and Hien Duy Nguyen and Faicel Chamroukhi and Florence Forbes}, journal={Electronic Journal of Statistics}, year={2022} }

: Mixture of experts (MoE) are a popular class of statistical and machine learning models that have gained attention over the years due to their ﬂexibility and eﬃciency. In this work, we consider Gaussian- gated localized MoE (GLoME) and block-diagonal covariance localized MoE (BLoME) regression models to present nonlinear relationships in het- erogeneous data with potential hidden graph-structured interactions between high-dimensional predictors. These models pose diﬃcult statistical…

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## References

SHOWING 1-10 OF 102 REFERENCES

### ℓ1-penalization for mixture regression models

- Mathematics, Computer Science
- 2010

We consider a finite mixture of regressions (FMR) model for high-dimensional inhomogeneous data where the number of covariates may be much larger than sample size. We propose an ℓ1-penalized maximum…

### An l1-oracle inequality for the Lasso in finite mixture Gaussian regression models

- Mathematics, Computer Science
- 2013

The aim is to extend the l 1 -oracle inequality established by Massart and Meynet in the homogeneous Gaussian linear regression case, and to present a complementary result to Stadler et al.

### High-dimensional regression with gaussian mixtures and partially-latent response variables

- Computer ScienceStat. Comput.
- 2015

An inverse regression framework is proposed, which exchanges the roles of input and response, such that the low-dimensional variable becomes the regressor, and which is tractable and Experimental evidence is provided that the method outperforms several existing regression techniques.

### An ℓ1-oracle inequality for the Lasso in multivariate finite mixture of multivariate Gaussian regression models

- Mathematics, Computer Science
- 2015

We consider a multivariate finite mixture of Gaussian regression models for high-dimensional data, where the number of covariates and the size of the response may be much larger than the sample size.…

### Hierarchical mixtures-of-experts for exponential family regression models: approximation and maximum

- Mathematics
- 1999

We consider hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form ψ(α + x T β) are mixed. Here ψ(.) is the…

### Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models

- Computer ScienceArXiv
- 2015

An application to a real gene expression dataset with a limited sample size is presented: the dimension reduction allows attention to be objectively focused on interactions among smaller subsets of genes, leading to a more parsimonious and interpretable modular network.

### Model-based regression clustering for high-dimensional data: application to functional data

- Computer ScienceAdv. Data Anal. Classif.
- 2017

This article investigates the relationship between response and predictors arising from different subpopulations using finite mixture regression models and proposes two procedures to cluster observations according to the link between predictors and the response.

### Data-driven Calibration of Penalties for Least-Squares Regression

- Computer Science, MathematicsJ. Mach. Learn. Res.
- 2009

A completely data-driven calibration algorithm for these parameters in the least-squares regression framework, without assuming a particular shape for the penalty, based on the concept of minimal penalty, recently introduced by Birge and Massart (2007).

### Data-driven penalty calibration : a case study for Gaussian mixture model selection

- Computer Science
- 2011

The behavior of the data-driven criterion, a penalized likelihood criterion to select a Gaussian mixture model among a specific model collection, is highlighted on simulated datasets, a curve clustering example and a genomics application.

### Inverse regression approach to robust nonlinear high-to-low dimensional mapping

- Computer ScienceJ. Multivar. Anal.
- 2018