Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data

Mathematics

• Expectation-Maximization Algorithm 100%
• Gaussian Mixture Model 100%
• Centroid 100%
• K-Means 100%
• Matrix (Mathematics) 50%
• Clustering Method 50%
• Model-Based Clustering 50%
• Objective Function 50%
• Covariance Matrix 50%
• Global Solution 50%
• Iterative Method 50%
• Log Likelihood 50%
• Dimensional Data 50%
• Observed Data 50%
• Mixture Distribution 50%
• Numerics 50%

Keyphrases

• Heterogeneous Data 100%
• Semidefinite Programming 100%
• K-means 40%
• Expectation-maximization Algorithm 40%
• Gaussian Mixture Model 40%
• Data Heterogeneity 40%
• Cluster Label 40%
• Centroid 20%
• Centroid Measurement 20%
• Computational Challenges 20%
• Unsupervised Learning 20%
• Clustering Methods 20%
• Nonconvex 20%
• Likelihood-free Inference 20%
• Covariance Matrix 20%
• Learning Tools 20%
• Model-based Clustering 20%
• Iterative Methods 20%
• Programming Method 20%
• Sharp Threshold 20%
• Local Maxima 20%
• High-dimensional Data 20%
• Group-specific 20%
• Log-likelihood Ratio 20%
• Expectation Maximization 20%
• Mixture Distribution 20%
• Programming Approaches 20%
• Clustering Error 20%
• Membership Matrix 20%
• Statistical Challenges 20%
• Exact Recovery 20%
• Cluster Assignment 20%
• Surrogate Function 20%
• Observed Likelihood 20%
• Numeric Experiments 20%
• Unique Global Solution 20%

Computer Science

• Heterogeneous Data 100%
• Semidefinite Programming 100%
• Gaussian Mixture Model 50%
• Data Heterogeneity 50%
• Objective Function 25%
• Clustering Method 25%
• Unsupervised Learning 25%
• Programming Method 25%
• Covariance Matrix 25%
• High Dimensional Data 25%
• Iterative Matrix 25%