visualizing Data using t-SNE: Difference between revisions

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=Introduction=
=Introduction=
The paper <ref>Laurens van der Maaten, and Geoffrey Hinton, 2008. Visualizing Data using t-SNE.</ref> introduced a new nonlinear dimensionally reduction technique that visualizes high-dimensional data based on the pair-wise similarities between the datapoints. This technique is a variation of the Stochastic Neighbor embedding that was proposed by Hinton and Roweis <ref>G.E. Hinton and S.T. Roweis, 2002. Stochastic Neighbor embedding.</ref>.


=Stochastic Neighbor Embedding=
=Stochastic Neighbor Embedding=

Revision as of 16:44, 12 July 2009

Introduction

The paper <ref>Laurens van der Maaten, and Geoffrey Hinton, 2008. Visualizing Data using t-SNE.</ref> introduced a new nonlinear dimensionally reduction technique that visualizes high-dimensional data based on the pair-wise similarities between the datapoints. This technique is a variation of the Stochastic Neighbor embedding that was proposed by Hinton and Roweis <ref>G.E. Hinton and S.T. Roweis, 2002. Stochastic Neighbor embedding.</ref>.

Stochastic Neighbor Embedding

t-Distributed Stochastic Neighbor Embedding

Symmetric SNE

The Crowding Problem

Compensating for Mismatched Dimensionality by Mismatched Tails

Optimization Methods for t-SNE

Experiments with Different Data Sets

t-SNE for Large Data Sets

Weaknesses of t-SNE

Summary

References