Identifying the number of components in Gaussian mixture models using numerical algebraic geometry
DOI10.1142/S0219498820502047zbMath1453.62553OpenAlexW2974781388WikidataQ114614542 ScholiaQ114614542MaRDI QIDQ5133853
Elizabeth Gross, S. Shirinkam, Adel Alaeddini
Publication date: 11 November 2020
Published in: Journal of Algebra and Its Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219498820502047
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical aspects of information-theoretic topics (62B10) Statistical aspects of big data and data science (62R07) Numerical algebraic geometry (65H14)
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- Critical points via monodromy and local methods
- HOM4PS-2.0: a software package for solving polynomial systems by the polyhedral homotopy continuation method
- Modeling by shortest data description
- The weighted average information criterion for order selection in time series and regression models
- Tensor decomposition and homotopy continuation
- Homotopy techniques for tensor decomposition and perfect identifiability
- Estimation and model selection for model-based clustering with the conditional classification likelihood
- The topography of multivariate normal mixtures
- A Bayesian Learning Coefficient of Generalization Error and Vandermonde Matrix-Type Singularities
- Maximum Likelihood Estimates for Gaussian Mixtures Are Transcendental
- Moment Varieties of Gaussian Mixtures
- Algorithm 921
- Maximum likelihood geometry in the presence of data zeros
- Order selection in finite mixture models: complete or observed likelihood information criteria?
- Bias of the corrected AIC criterion for underfitted regression and time series models
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- GMM Estimation with persistent panel data: an application to production functions
- Algorithm 795
- Model Selection and Multimodel Inference
- Algebraic Identifiability of Gaussian Mixtures
- A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION
- The Numerical Solution of Systems of Polynomials Arising in Engineering and Science
- A Bayesian Information Criterion for Singular Models
- A practical guide to splines.
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