Abstract
Recently, there has been an increasing interest in developing statistical
methods able to find groups in matrix-valued data. To this extent, matrix Gaussian
mixture models (MGMM) provide a natural extension to the popular model-based
clustering based on Normal mixtures. Unfortunately, the overparametrization issue,
already affecting the vector-variate framework, is further exacerbated when it comes
to MGMM, since the number of parameters scales quadratically with both row and
column dimensions. In order to overcome this limitation, the present paper introduces
a sparse model-based clustering approach for three-way data structures. By
means of penalized estimation, our methodology shrinks the estimates towards zero,
achieving more stable and parsimonious clustering in high dimensional scenarios.
An application to satellite images underlines the benefits of the proposed method.
Lingua originale | English |
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Titolo della pubblicazione ospite | Book of Short Papers SIS 2021 |
Pagine | 758-763 |
Numero di pagine | 6 |
Stato di pubblicazione | Pubblicato - 2021 |
Evento | Scientific Meeting of the Italian Statistical Society - Pisa Durata: 21 giu 2021 → 25 giu 2021 |
Convegno
Convegno | Scientific Meeting of the Italian Statistical Society |
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Città | Pisa |
Periodo | 21/6/21 → 25/6/21 |
Keywords
- Model based clustering
- Matrix distribution
- Sparse matrix estimation
- penalized likelihood
- EM-algorithm