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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, :visualization,dimensionalityreduction,m anifoldlearning,embeddingalgorithms, differentdomains,anddealswithdataofwidel yvaryingdimensionality.

techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualiza-tions produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets. Keywords: visualization, dimensionality reduction, manifold learning, embedding algorithms, multidimensional scaling 1. Introduction

  Linear, Embedding, Linear embedding

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