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Research map / Learning theory and algorithms / Clustering

Supervised Hierarchical Clustering with Exponential Linkage

ICML 2019 · Learning theory and algorithms › Clustering · approach: Clustering approximation algorithms · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace all-pairs training with MST-based trainingSingle-linkage HAC · Faces, NP Coref, Rexa, AMINERdendrogram purityHelped
Match the training procedure to the linkage functionSupervised HAC · Four datasetsdendrogram purity: up to 8 pointsHelped
Jointly learn the exponential-linkage parameter and pairwise dissimilarityExponential Linkage HAC · AMINERdendrogram purityHelped
Jointly learn the exponential-linkage parameter and pairwise dissimilarityExponential Linkage HAC · NP Corefdendrogram purityHelped
Jointly learn the exponential-linkage parameter and pairwise dissimilarity rather than use all-pairs trainingExponential Linkage HAC · Rexapairwise F1: more than 20 points higher F1Helped
Use all-pairs training instead of the best-performing training procedureSingle-linkage and Exponential Linkage HAC · Rexa, NP Coref, Facesdendrogram purity: 4-8%Hurt
Use all-pairs training instead of the best-performing training procedureAverage-linkage HAC · Four datasetsdendrogram purityHurt
Use complete linkage rather than another tested linkageSupervised HAC · Four datasetsdendrogram purityHurt
Train with expAlpha rather than all-pairsExponential Linkage HAC · Rexapairwise F1: more than 20 points higher F1Helped
Use the best-performing method rather than expPlusSupervised HAC · AMINERpairwise F1: ~20 points F1Helped
Use exponential linkage with alpha = -1 instead of single, average, or complete linkageHAC with Euclidean distance · Synthetic three-cluster datasetcluster recoveryHelped
Choose an exponential-linkage member after training the dissimilarity function rather than fixing a standard linkage beforehandSupervised HAC · Rexa, Facesdendrogram purityMixed

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