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Section: Overall Objectives
Highlights
Since November 2011, the team started the development of the co-clustering module in the MIXMOD software, allowing to process efficient and parsimonious generative models on huge data sets (see Section 5.1 ).
Team
Modal
Overall Objectives
Scientific Foundations
Application Domains
New Results
- Intermediate dependency generative models
- Transfer learning in model-based clustering
- Block regression for variable clustering: Application to genetic data
- Label switching in mixtures
- Degeneracy in Gaussian mixtures
- Wavelet based clustering using mixed effects functional models
- Comparison of normalisation procedures in RNA-sequencing before differential analysis
- Comparison of peak finding methods applied to tiling array experiments
- Model-based clustering for functional data
- Generative models and random graphs
- Resampling and learning
Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
Team
Modal
Overall Objectives
Scientific Foundations
Application Domains
New Results
- Intermediate dependency generative models
- Transfer learning in model-based clustering
- Block regression for variable clustering: Application to genetic data
- Label switching in mixtures
- Degeneracy in Gaussian mixtures
- Wavelet based clustering using mixed effects functional models
- Comparison of normalisation procedures in RNA-sequencing before differential analysis
- Comparison of peak finding methods applied to tiling array experiments
- Model-based clustering for functional data
- Generative models and random graphs
- Resampling and learning
Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography