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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.

multidimensional scaling 1. Introduction Visualization of high-dimensional data is an important problem in many different domains, and deals with data of widely varying dimensionality. Cell nuclei that are relevant to breast cancer, for example, are described by approximately 30 variables (Street et al., 1993), whereas the pixel ...

  Data, Visualization, Multidimensional

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