Abstract
We introduce a latent subpace model which facilitates model-based clustering of functional data. Flexible clustering is attained by imposing jointly generalized hyperbolic distributions on projections of basis expansion coefficients into group specific subspaces. The model acquires parsimony by assuming these subspaces are of relatively low dimension. Parameter estimation is done through a multicycle ECM algorithm. Application to simulated and real datasets illustrate competitive clustering capabilities, and demonstrate the models general applicability.
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Sharp, A., Browne, R. Functional data clustering by projection into latent generalized hyperbolic subspaces. Adv Data Anal Classif 15, 735–757 (2021). https://doi.org/10.1007/s11634-020-00432-5
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DOI: https://doi.org/10.1007/s11634-020-00432-5
Keywords
- Model-based clustering
- Functional data analysis
- Dimension reduction
- Functional principal component analysis
- EM algorithm