Simultaneous Component and Clustering Models for Three-way Data: Within and Between Approaches
β Scribed by Maurizio Vichi; Roberto Rocci; Henk A.L. Kiers
- Publisher
- Springer
- Year
- 2007
- Tongue
- English
- Weight
- 274 KB
- Volume
- 24
- Category
- Article
- ISSN
- 0176-4268
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Multilinear analysis methods such as component (and three-way component) analysis of very large data sets can become very computationally demanding and even infeasible unless some method is used to compress the data and/or speed up the algorithms. We discuss two previously proposed speedup methods.
The relationship between marginal (population-averaged) models for cluster-correlated binary data, and a class of cluster-speciΓΏc, logistic-normal random e ects models is discussed. We show that random e ects models can accomplish the same end as a more direct modelling of intra-cluster correlation,
## Ε½ . Ε½ . Direct trilinear decomposition DTD and multivariate curve resolution-alternating least squares MCR-ALS methods are two of the most representative three-way resolution procedures. The former, non-iterative, is based on the resolution of the generalized eigenvectorreigenvalue problem and