Search Results for author:
Found 31 papers, 4 papers with code
Functional mixture-of-experts for classification
We develop a mixtures-of-experts (ME) approach to the multiclass classification where the predictors are univariate functions.
Spectral image clustering on dual-energy CT scans using functional regression mixtures
In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves.
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models
This model selection criterion allows us to handle the challenging problem of inferring the number of mixture components, the degree of polynomial mean functions, and the hidden block-diagonal structures of the covariance matrices, which reduces the number of parameters to be estimated and leads to a trade-off between complexity and sparsity in the model.
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models
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.
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts
Mixture of experts (MoE) has a well-principled finite mixture model construction for prediction, allowing the gating network (mixture weights) to learn from the predictors (explanatory variables) together with the experts' network (mixture component densities).
Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models
Mixtures-of-Experts (MoE) are conditional mixture models that have shown their performance in modeling heterogeneity in data in many statistical learning approaches for prediction, including regression and classification, as well as for clustering.
Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models
Mixture of Experts (MoE) are successful models for modeling heterogeneous data in many statistical learning problems including regression, clustering and classification.
Model-Based Clustering and Classification of Functional Data
FDA is the data analysis paradigm in which the individual data units are functions (e. g., curves, surfaces), rather than simple vectors.
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
Due to the probabilistic nature of MoE models, we propose the maximum quasi-likelihood (MQL) estimator as a method for estimating MoE model parameters from data, and we provide conditions under which MQL estimators are consistent and asymptotically normal.
Robust mixture of experts modeling using the skew $t$ distribution
Mixture of Experts (MoE) is a popular framework in the fields of statistics and machine learning for modeling heterogeneity in data for regression, classification and clustering.
Robust mixture of experts modeling using the $t$ distribution
For regression and cluster analyses of continuous data, MoE usually use normal experts following the Gaussian distribution.
Bayesian mixtures of spatial spline regressions
Then, in order to model populations of spatial functional data issued from heterogeneous groups, we integrate the BSSR model into a mixture framework.
Non-Normal Mixtures of Experts
Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification and clustering.
Dirichlet Process Parsimonious Mixtures for clustering
The proposed DPPM models are Bayesian nonparametric parsimonious mixture models that allow to simultaneously infer the model parameters, the optimal number of mixture components and the optimal parsimonious mixture structure from the data.
Unsupervised learning of regression mixture models with unknown number of components
The proposed learning approach is fully unsupervised: 1) it simultaneously infers the model parameters and the optimal number of the regression mixture components from the data as the learning proceeds, rather than in a two-fold scheme as in standard model-based clustering using afterward model selection criteria, and 2) it does not require accurate initialization unlike the standard EM for regression mixtures.
A regression model with a hidden logistic process for signal parametrization
A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed.
A regression model with a hidden logistic process for feature extraction from time series
The parameters of the hidden logistic process, in the inner loop of the EM algorithm, are estimated using a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm.
Robust EM algorithm for model-based curve clustering
Our approach both handles the problem of initialization and the one of choosing the optimal number of clusters as the EM learning proceeds, rather than in a two-fold scheme.
Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time
This paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements.
Mixture model-based functional discriminant analysis for curve classification
More specifically, we propose a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model.
Model-based clustering and segmentation of time series with changes in regime
The proposed approach can also be regarded as a clustering approach which operates by finding groups of time series having common changes in regime.
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
This paper presents a new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors.
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc.
A hidden process regression model for functional data description. Application to curve discrimination
A new approach for functional data description is proposed in this paper.
Classification automatique de données temporelles en classes ordonnées
This paper proposes a method of segmenting temporal data into ordered classes.
Time series modeling by a regression approach based on a latent process
A new approach for time series modeling is proposed in this paper.
Model-based functional mixture discriminant analysis with hidden process regression for curve classification
We propose a new model-based functional mixture discriminant analysis approach based on a specific hidden process regression model that governs the regime changes over time.
Model-based clustering with Hidden Markov Model regression for time series with regime changes
Comparisons with existing approaches for time series clustering, including the stand EM for Gaussian mixtures, $K$-means clustering, the standard mixture of regression models and mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed approach.
Functional Mixture Discriminant Analysis with hidden process regression for curve classification
We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model.
Piecewise regression mixture for simultaneous functional data clustering and optimal segmentation
The proposed approach is evaluated using simulated curves and real-world curves.
Modèle à processus latent et algorithme EM pour la régression non linéaire
A non linear regression approach which consists of a specific regression model incorporating a latent process, allowing various polynomial regression models to be activated preferentially and smoothly, is introduced in this paper.
