Detecting Multivariate Outliers Using Projection Pursuit with Particle Swarm Optimization
DOI10.1007/978-3-7908-2604-3_8zbMath1436.62306OpenAlexW2215345492MaRDI QIDQ3298450
Souad Larabi Marie-Sainte, Anne Ruiz-Gazen, Alain Berro
Publication date: 14 July 2020
Published in: Proceedings of COMPSTAT'2010 (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-7908-2604-3_8
heuristic algorithmsprojection pursuitparticle swarm optimizationmultivariate outliers detectiontribes algorithm
Computational methods for problems pertaining to statistics (62-08) Multivariate analysis (62H99) Factor analysis and principal components; correspondence analysis (62H25) Approximation methods and heuristics in mathematical programming (90C59)
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Cites Work
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- Invariant Co-Ordinate Selection
- MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm
- High breakdown estimators for principal components: the projection-pursuit approach revis\-ited
- Genetic algorithms and particle swarm optimization for exploratory projection pursuit
- A projection pursuit algorithm for anomaly detection in hyperspectral imagery
- A monitoring display of multivariate outliers
- Projection pursuit
- Principal component analysis.
- On polynomial-based projection indices for exploratory projection pursuit
- Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo
- What is Projection Pursuit?
- Exploratory Projection Pursuit
- Significance levels in exploratory projection pursuit
- A Projection Pursuit Algorithm for Exploratory Data Analysis
- The Behavior of the Stahel-Donoho Robust Multivariate Estimator
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