Mixtures Closest to a Given Measure: A Semidefinite Programming Approach | Paper Citation Record

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This paper cites A new look at the statistical model identification.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach A new look at the statistical model identification

Reference 1

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This paper cites Algebraic identifiability of gaussian mixtures.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Algebraic identifiability of gaussian mixtures

Reference 2

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This paper cites The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm

Reference 3

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Antoniak

Reference 4

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This paper cites Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models

Reference 5

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This paper cites k-means++: the advantages of careful seeding.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach k-means++: the advantages of careful seeding

Reference 6

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This paper cites Multimodal word distributions.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Multimodal word distributions

Reference 7

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This paper cites Kane, Pravesh K.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Kane, Pravesh K

Reference 8

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 9

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This paper cites Aggregating continuous word embeddings for information retrieval.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Aggregating continuous word embeddings for information retrieval

Reference 10

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This paper cites Curto and Lawrence A.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Curto and Lawrence A

Reference 11

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This paper cites Learning mixtures of gaussians.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Learning mixtures of gaussians

Reference 12

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 13

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This paper cites Likelihood ratio tests and singularities.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Likelihood ratio tests and singularities

Reference 14

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This paper cites Model-based clustering, discriminant analysis, and density estimation.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Model-based clustering, discriminant analysis, and density estimation

Reference 15

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 16

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 17

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 18

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This paper cites Large sample properties of generalized method of moments estimators.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Large sample properties of generalized method of moments estimators

Reference 19

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This paper cites Strong identifiability and optimal minimax rates for finite mixture estimation.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Strong identifiability and optimal minimax rates for finite mixture estimation

Reference 20

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Observation 9b22f579-f355-4dde-a852-b5e4be2b7468 · outbound

This paper cites Detecting Global Optimality and Extracting Solutions in GloptiPoly, pp.\ 293--310.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Detecting Global Optimality and Extracting Solutions in GloptiPoly, pp.\ 293--310

Reference 21

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This paper cites Matrix manifold optimization for gaussian mixtures.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Matrix manifold optimization for gaussian mixtures

Reference 22

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This paper cites Bootstrapping log likelihood and eic, an extension of aic.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Bootstrapping log likelihood and eic, an extension of aic

Reference 23

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This paper cites Finite mixtures of multivariate poisson distributions with application.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Finite mixtures of multivariate poisson distributions with application

Reference 24

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This paper cites Choosing initial values for the em algorithm for finite mixtures.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Choosing initial values for the em algorithm for finite mixtures

Reference 25

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Rousseeuw

Reference 26

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This paper cites Tensor decomposition for learning gaussian mixtures from moments.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Tensor decomposition for learning gaussian mixtures from moments

Reference 27

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Kiefer and J

Reference 28

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This paper cites Minimizer extraction in polynomial optimization is robust.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Minimizer extraction in polynomial optimization is robust

Reference 29

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This paper cites Kothari, Prasad Raghavendra Manohar, and B.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Kothari, Prasad Raghavendra Manohar, and B

Reference 30

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Lasserre

Reference 31

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This paper cites Moments, Positive Polynomials and Their Applications.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Moments, Positive Polynomials and Their Applications

Reference 32

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This paper cites An Introduction to Polynomial and Semi-Algebraic Optimization.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach An Introduction to Polynomial and Semi-Algebraic Optimization

Reference 33

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This paper cites A hierarchy of convex relaxations for the total variation distance.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach A hierarchy of convex relaxations for the total variation distance

Reference 34

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This paper cites Estimating gaussian mixtures using sparse polynomial moment systems.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Estimating gaussian mixtures using sparse polynomial moment systems

Reference 35

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 36

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Clustering mixtures with almost optimal separation in polynomial time

Reference 37

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Likelihood ratio tests of the number of components in a normal mixture with unequal variances

Reference 38

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This paper cites TSSOS: a Julia library to exploit sparsity for large-scale polynomial optimization.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach TSSOS: a Julia library to exploit sparsity for large-scale polynomial optimization

Reference 39

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach McLachlan and David Peel

Reference 40

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach McLachlan and Suren Rathnayake

Reference 41

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Moitra and G

Reference 42

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 43

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Tensor Moments of Gaussian Mixture Models: Theory and Applications

Reference 44

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This paper cites A study of gaussian mixture models of color and texture features for image classification and segmentation.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach A study of gaussian mixture models of color and texture features for image classification and segmentation

Reference 45

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Positive polynomials on compact semi-algebraic sets

Reference 46

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

Reference 47

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Reynolds, Thomas F

Reference 48

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Lasserre

Reference 49

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Estimating the dimension of a model

Reference 50

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach A cautionary note on likelihood ratio tests in mixture models

Reference 51

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This paper cites Identifying the number of components in gaussian mixture models using numerical algebraic geometry.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Identifying the number of components in gaussian mixture models using numerical algebraic geometry

Reference 52

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Estimating the number of clusters in a data set via the gap statistic

Reference 54

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Optimal estimation of gaussian mixtures via denoised method of moments

Reference 56

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This paper cites Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow

Reference 57

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This paper cites Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity

Reference 58

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This paper cites A mixture of mixture models for a classification problem: The unity measure error.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach A mixture of mixture models for a classification problem: The unity measure error

Reference 59

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This paper cites @esa (Ref.

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach @esa (Ref

Reference 60

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach Unresolved cited work

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Mixtures Closest to a Given Measure: A Semidefinite Programming Approach A X . V@ly`r?4

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