Classification automatique de données temporelles en classes ordonnées
This work addresses a domain-specific problem in temporal data analysis, offering incremental improvements in algorithm speed for ordered classification tasks.
The paper tackles the problem of segmenting temporal data into ordered classes using mixture models with a discrete latent process, resulting in improved computational efficiency compared to Fisher's algorithm.
This paper proposes a method of segmenting temporal data into ordered classes. It is based on mixture models and a discrete latent process, which enables to successively activates the classes. The classification can be performed by maximizing the likelihood via the EM algorithm or by simultaneously optimizing the model parameters and the partition by the CEM algorithm. These two algorithms can be seen as alternatives to Fisher's algorithm, which improve its computing time.