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Gaussian Mixture Models

Amritesh Kumar by Amritesh Kumar
January 29, 2024 - Updated on February 24, 2024
in Machine Learning, Premium
Reading Time: 6 mins read
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Gaussian Mixture Models

Gaussian Mixture Models related concepts are explained in the notebook below. Please practice and ask your questions in the forum if any. Please don’t share the notebook anywhere without our permission.

Table of Contents

  • Gaussian Mixture Models: Concept And Application
  • Gaussian Mixture Model (GMM) vs. K-Means:
    • Comparison
    • Advantages and Disadvantages:
      • K-Means:
      • GMM:
    • When to Use Which:

Gaussian Mixture Models: Concept And Application

gaussian-mixture-models

Gaussian Mixture Model (GMM) vs. K-Means:

Comparison

ComparisonK-MeansGMM
ObjectivePartition data into K clusters with nearest meanModel data as a mixture of Gaussian distributions
Cluster ShapeSpherical and equally sized clustersFlexible, accommodates varied shapes and sizes
Uncertainty and Probabilistic AssignmentProvides hard assignmentsProvides probabilistic assignments (soft clustering)
AssumptionAssumes equal variance and isotropic clustersAllows for different variances and covariances
Initialization SensitivitySensitive to initial centroid placementLess sensitive to initialization

Advantages and Disadvantages:

K-Means:

AspectAdvantagesDisadvantages
OverviewSimple and computationally efficientSensitive to outliers
Cluster ShapeWorks well for well-separated, spherical clustersAssumes clusters are of similar size and shape
ScalabilityScalable to large datasetsLimited to linear cluster boundaries

GMM:

AspectAdvantagesDisadvantages
FlexibilityMore flexible in accommodating varied cluster shapes and sizesComputationally more intensive than K-Means
UncertaintyProvides uncertainty measures through probabilistic assignmentsMay converge to a local minimum, sensitive to initialization
Overlapping ClustersCan model overlapping clustersRequires estimating more parameters

When to Use Which:

ScenarioK-MeansGMM
Cluster Shape AssumptionIsotropic, equally sized clustersDifferent shapes or sizes are present
Computational EfficiencySimple and computationally efficientTolerant to higher computational cost
Cluster AssignmentsHard cluster assignments are sufficientSoft (probabilistic) cluster assignments are desired
Tags: ClusteringGMMK-MeansUnsupervised Learning
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Amritesh Kumar

Amritesh Kumar

I believe you are not dumb or unintelligent; you just never had someone who could simplify the concepts you struggled to understand. My goal here is to simplify AI for all. Please help me improve this platform by contributing your knowledge on machine learning and data science, or help me improve current tutorials. I want to keep all the resources free except for support and certifications. Email me @amriteshkr18@gmail.com.

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