Visualizing Data using t-SNE

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JournalofMachineLearningResearch9 ,5000LETilburg, King s College Road,M5S3G4Toronto,ON,CanadaEditor:Yoshu aBengioAbstractWe 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.

VISUALIZING DATA USING T-SNE 2. Stochastic Neighbor Embedding Stochastic Neighbor Embedding (SNE) starts by converting the high-dimensional Euclidean dis-tances between datapoints into conditional probabilities that represent similarities.1 The similarity of datapoint xj to datapoint xi is the conditional probability, pjji, that xi would pick xj as its neighbor

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