Unobserved classes and extra variables in high-dimensional discriminant analysis
From MaRDI portal
Publication:2673359
DOI10.1007/s11634-021-00474-3OpenAlexW4220953254MaRDI QIDQ2673359
Charles Bouveyron, Michael Fop, Pierre-Alexandre Mattei, Thomas Brendan Murphy
Publication date: 9 June 2022
Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2102.01982
variable selectionunobserved classesconditional estimationmodel-based discriminant analysisadaptive supervised classification
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Adaptive mixture discriminant analysis for supervised learning with unobserved classes
- Exact and approximate algorithms for variable selection in linear discriminant analysis
- Variable selection in model-based discriminant analysis
- An adapted linear discriminant analysis with variable selection for the classification in high-dimension, and an application to medical data
- Novelty detection: a review. I. Statistical approaches
- Modified linear discriminant analysis approaches for classification of high-dimensional microarray data
- Variable selection in model-based clustering: a general variable role modeling
- Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications
- Analysis of new variable selection methods for discriminant analysis
- The multivariate normal distribution
- Estimating the dimension of a model
- General sparse multi-class linear discriminant analysis
- Variable selection methods for model-based clustering
- Anomaly and novelty detection for robust semi-supervised learning
- Improved initialisation of model-based clustering using Gaussian hierarchical partitions
- Generalized mixture models, semi-supervised learning, and unknown class inference
- Variable Selection for Clustering with Gaussian Mixture Models
- A direct approach to sparse discriminant analysis in ultra-high dimensions
- The EM Algorithm and Extensions, 2E
- Estimation of a covariance matrix with zeros
- Algorithms for Model-Based Gaussian Hierarchical Clustering
- Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- 10.1162/153244303322753616
- General Subspace Learning With Corrupted Training Data Via Graph Embedding
- Model-Based Clustering and Classification for Data Science
- Variable Selection for Model-Based Clustering
- EM for mixtures
This page was built for publication: Unobserved classes and extra variables in high-dimensional discriminant analysis