Anomaly and novelty detection for robust semi-supervised learning
From MaRDI portal
Publication:2029069
DOI10.1007/s11222-020-09959-1zbMath1461.62015arXiv1911.08381OpenAlexW3102615300MaRDI QIDQ2029069
Thomas Brendan Murphy, Andrea Cappozzo, Francesca Greselin
Publication date: 3 June 2021
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.08381
inductive inferencerobust estimationmodel-based classificationoutliers detectionnovelty detectionunobserved classeslabel noiseimpartial trimmingtransductive inference
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Related Items (3)
Unobserved classes and extra variables in high-dimensional discriminant analysis ⋮ Consistency factor for the MCD estimator at the Student-\(t\) distribution ⋮ A two-stage Bayesian semiparametric model for novelty detection with robust prior information
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models
- Robust fitting of mixtures using the trimmed likelihood estimator
- Adaptive mixture discriminant analysis for supervised learning with unobserved classes
- Robust estimation of the number of components for mixtures of linear regression models
- Robust estimation of mixtures of regressions with random covariates, via trimming and constraints
- Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints
- A general trimming approach to robust cluster analysis
- Novelty detection: a review. I. Statistical approaches
- Using combinatorial optimization in model-based trimmed clustering with cardinality constraints
- Best approximations to random variables based on trimming procedures
- Estimating the dimension of a model
- The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family
- The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers
- Variational inference for probabilistic Poisson PCA
- Class noise vs. attribute noise: A quantitative study of their impacts
- A constrained robust proposal for mixture modeling avoiding spurious solutions
- Robust supervised classification with mixture models: learning from data with uncertain labels
- Weighted likelihood mixture modeling and model-based clustering
- Comments on ``The power of monitoring: how to make the most of a contaminated multivariate sample
- Eigenvalues and constraints in mixture modeling: geometric and computational issues
- Robust clusterwise linear regression through trimming
- A likelihood-based constrained algorithm for multivariate normal mixture models
- Optimal feature selection for support vector machines
- Noise modelling and evaluating learning from examples
- Detecting Wine Adulterations Employing Robust Mixture of Factor Analyzers
- Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods
- Degeneracy in the Maximum Likelihood Estimation of Univariate Gaussian Mixtures for Grouped Data and Behaviour of the EM Algorithm
- Model-Based Gaussian and Non-Gaussian Clustering
- High-Breakdown Linear Discriminant Analysis
- Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Wild adaptive trimming for robust estimation and cluster analysis
- Estimating the components of a mixture of normal distributions
- Using Unlabelled Data to Update Classification Rules with Applications in Food Authenticity Studies
- Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices
- Avoiding spurious local maximizers in mixture modeling
- A new look at the statistical model identification
This page was built for publication: Anomaly and novelty detection for robust semi-supervised learning
