A regression model with a hidden logistic process for feature extraction\n from time series

2013/12/25 by Faïcel Chamroukhi, Chamroukhi, Faicel, Allou Samé +5
Chemistry · Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Neural Networks and Applications #Spectroscopy and Chemometric Analyses #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1312.7001

openalex publication_date 2013/12/25 · openalex created_date 2022/08/19 · openalex updated_date 2026/07/28

Abstract

A new approach for feature extraction from time series is proposed in this\npaper. This approach consists of a specific regression model incorporating a\ndiscrete hidden logistic process. The model parameters are estimated by the\nmaximum likelihood method performed by a dedicated Expectation Maximization\n(EM) algorithm. The parameters of the hidden logistic process, in the inner\nloop of the EM algorithm, are estimated using a multi-class Iterative\nReweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm and\nits iterative variant have also been considered for comparisons. An\nexperimental study using simulated and real data reveals good performances of\nthe proposed approach.\n

Citations

• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
• A tutorial on hidden Markov models and selected applications in speech recognition

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