Functional Mixture Discriminant Analysis with hidden process regression for curve classification
This work addresses curve classification in functional data analysis, but it appears incremental as it builds on existing mixture and regression models.
The authors tackled the problem of classifying complex-shaped curves with regime changes by proposing a new mixture model-based discriminant analysis approach using hidden process regression, which showed better results compared to alternative methods on simulated data.
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.