Functional Mixture Discriminant Analysis with hidden process regression for curve classification

2013/12/25 by Faïcel Chamroukhi, Faicel Chamroukhi, Chamroukhi, Faicel +4
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods and Inference #cs.LG #stat.ME #stat.ML

paper · pdf · doi:10.48550/arxiv.1312.7007

In Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Pages 281-286, 2012, Bruges, Belgium

arxiv created 2013/12/25 · openalex publication_date 2013/12/25 · arxiv updated 2013/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible curve-models to each class of complex-shaped curves presenting regime changes. The model parameters are learned by maximizing the observed-data log-likelihood for each class by using a dedicated expectation-maximization (EM) algorithm. Comparisons on simulated data with alternative approaches show that the proposed approach provides better results.

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