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Section: New Software and Platforms
PACBayesianNMF
Keywords: Statistics - Machine learning
Functional Description: Implementing NMF with a PAC-Bayesian approach relying upon block gradient descent
Research Program
Application Domains
New Software and Platforms
New Results
- Axis 1: Data Units Selection in Statistics
- Axis 1: Model-Based Co-clustering for Ordinal Data
- Axis 1: Model-Based Co-clustering for Ordinal Data of different dimensions
- Axis 1: Model-based co-clustering for mixed type data
- Axis 1: Model-Based Co-clustering with Co-variables
- Axis 1: Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data
- Axis 1: Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering
- Axis 1: A targeted multi-partitions clustering
- Axis 1: Co-clustering: A versatile way to perform clustering in high dimension
- Axis 1: Dealing with missing data in model-based clustering through a MNAR model
- Axis 1: Self Organizing Coclustering for textual data synthesis
- Axis 1: Linking canonical and spectral clustering
- Axis 1: Multiple partition clustering
- Axis 2: Change-point detection by means of reproducing kernels
- Axis 2: New efficient algorithms for multiple change-point detection with kernels
- Axis 2: Multi-Layer Group-Lasso
- Axis 2: Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
- Axis 2: Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
- Axis 2: Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
- Axis 2: A Quasi-Bayesian Perspective to Online Clustering
- Axis 2: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
- Axis 2: Simpler PAC-Bayesian bounds for hostile data
- Axis 2: PAC-Bayesian high dimensional bipartite ranking
- Axis 2: Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
- Axis 3: Clustering spatial functional data
- Axis 3: Categorical functional data analysis
- Axis 4: Real-time Audio Sources Classification
- Axis 4: Matching of descriptors evolving over time
- Axis 4: Supervised multivariate discretization and levels merging for logistic regression
- Axis 4: MASSICCC Platform for SaaS Software Availability
- Axis 4: ClinMine: Optimizing the Management of Patients in Hospital
- Projection Under Pairwise Control
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
Research Program
Application Domains
New Software and Platforms
New Results
- Axis 1: Data Units Selection in Statistics
- Axis 1: Model-Based Co-clustering for Ordinal Data
- Axis 1: Model-Based Co-clustering for Ordinal Data of different dimensions
- Axis 1: Model-based co-clustering for mixed type data
- Axis 1: Model-Based Co-clustering with Co-variables
- Axis 1: Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data
- Axis 1: Gaussian-based visualization of Gaussian and non-Gaussian model-based clustering
- Axis 1: A targeted multi-partitions clustering
- Axis 1: Co-clustering: A versatile way to perform clustering in high dimension
- Axis 1: Dealing with missing data in model-based clustering through a MNAR model
- Axis 1: Self Organizing Coclustering for textual data synthesis
- Axis 1: Linking canonical and spectral clustering
- Axis 1: Multiple partition clustering
- Axis 2: Change-point detection by means of reproducing kernels
- Axis 2: New efficient algorithms for multiple change-point detection with kernels
- Axis 2: Multi-Layer Group-Lasso
- Axis 2: Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
- Axis 2: Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
- Axis 2: Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
- Axis 2: A Quasi-Bayesian Perspective to Online Clustering
- Axis 2: Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
- Axis 2: Simpler PAC-Bayesian bounds for hostile data
- Axis 2: PAC-Bayesian high dimensional bipartite ranking
- Axis 2: Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
- Axis 3: Clustering spatial functional data
- Axis 3: Categorical functional data analysis
- Axis 4: Real-time Audio Sources Classification
- Axis 4: Matching of descriptors evolving over time
- Axis 4: Supervised multivariate discretization and levels merging for logistic regression
- Axis 4: MASSICCC Platform for SaaS Software Availability
- Axis 4: ClinMine: Optimizing the Management of Patients in Hospital
- Projection Under Pairwise Control
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography