MODAL - 2018 - Annual activity report

MODAL

MODAL - 2018

Project-Team Modal

Team, Visitors, External Collaborators

Overall Objectives

Context
Goals

Research Program

Research axis 1: Unsupervised learning
Research axis 2: Performance assessment
Research axis 3: Functional data
Research axis 4: Applications motivating research

Application Domains

Economic World
Biology

Highlights of the Year

New Software and Platforms

MixtComp
BlockCluster
CloHe
PACBayesianNMF
pycobra
STK++
rtkore
MixAll
simerge
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

Bilateral Contracts: SEMENCES DE FRANCE
Bilateral Contracts: Arcelor-Mittal
Bilateral Contracts: Alstom
Bilateral Contracts: Decathlon

Partnerships and Cooperations

Regional Initiatives
National Initiatives
European Initiatives
International Initiatives
International Research Visitors

Dissemination

Promoting Scientific Activities
Teaching - Supervision - Juries
Popularization

Bibliography

Major publications
Publications of the year

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Section: Bilateral Contracts and Grants with Industry

Bilateral Contracts: Arcelor-Mittal

Participants : Christophe Biernacki, Vincent Vandewalle.

Arcelor-Mittal is a leader company in steel industry. This contract (which began in 2016 and finished in 2018) aims at optimizing predictive maintenance from mixed data (continuous, categorical, functional) provided by multiple sensors disseminated in steel production lines. Several thousands of sensors are simultaneously involved in this study, most of them providing functional (chronological) values.

It is a joint work with Quentin Grimonprez and Vincent Kubicki (InriaTech engineers).

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