Visualizing Data using t-SNE - Journal of Machine Learning ...
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.
Geoffrey Hinton HINTON@CS.TORONTO.EDU Department of Computer Science University of Toronto 6 King’s College Road, M5S 3G4 Toronto, ON, Canada Editor: Yoshua Bengio Abstract We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map.
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