Transcription of Visualizing Data using t-SNE - Laurens van der Maaten
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Visualizing Data using t-SNE - Laurens van der Maaten
JournalofMachineLearningResearch9 ,5000 LETilburg, King s College Road,M5S3G4 Toronto,ON,CanadaEditor:YoshuaBengioAbst ractWe presenta newtechniquecalled t-SNE thatvisualizeshigh-dimensionaldatabygivi ngeachdatapointa locationina two a variationofStochasticNeighborEmbedding(H intonandRoweis,2002)thatismucheasiertoop timize,andproducessignificantlybettervis ualizationsbyreducingthetendency betterthanexistingtechniquesat creatinga singlemapthatrevealsstructureat many particularlyimportantforhigh- dimensional datathatlieonseveraldifferent,butrelated ,low-dimensionalmanifolds, ,weshow howt-SNEcanuserandomwalksonneighborhoodg raphstoallowtheimplicitstructureofalloft hedatato influencethewayin whicha subsetofthedatais illustratetheperformanceoft-SNEona widevarietyofdatasetsandcompareit withmany othernon-parametricvisualizationtechniqu es,includingSammonmapping,Isomap.
high-dimensional space, σi). The remaining parameter to be selected is the variance σi of the Gaussian that is centered over each high-dimensional datapoint, xi. It is not likely that there is a single value of σi that is optimal for all datapoints in the data set because the density of the data is likely to vary. In dense regions,
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