Functional non-parametric latent block model: a multivariate time series clustering approach for autonomous driving validation
From MaRDI portal
Publication:2674522
DOI10.1016/J.CSDA.2022.107565OpenAlexW4285498907MaRDI QIDQ2674522
Etienne Goffinet, Anthony Coutant, Mustapha Lebbah, Hanane Azzag, Giraldi Loïc
Publication date: 14 September 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2022.107565
model-based clusteringtime series analysisDirichlet process mixture modelco-clusteringlatent block modelautonomous driving development
Related Items (2)
Editorial for the 2nd special issue on high-dimensional and functional data analysis ⋮ A survey on model-based co-clustering: high dimension and estimation challenges
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations
- Block clustering with collapsed latent block models
- Block clustering with Bernoulli mixture models: comparison of different approaches
- NP-hard problems in hierarchical-tree clustering
- Model-based co-clustering for ordinal data
- Co-clustering of time-dependent data via the shape invariant model
- Functional data analysis.
- A Review on Consensus Clustering Methods
- Comparison of Times Series with Unequal Length in the Frequency Domain
- Clustering for binary data and mixture models—choice of the model
- Estimating Normal Means with a Dirichlet Process Prior
- Estimation and selection for the latent block model on categorical data
- Variational inference for Dirichlet process mixtures
This page was built for publication: Functional non-parametric latent block model: a multivariate time series clustering approach for autonomous driving validation
