visualizing Data using t-SNE: Difference between revisions

From statwiki
Jump to navigation Jump to search
(Created page with '=Introduction= =Stochastic Neighbor Embedding= =t-Distributed Stochastic Neighbor Embedding= == Symmetric SNE == == The Crowding Problem == == Compensating for Mismatched ...')
 
Line 1: Line 1:
=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 15:44, 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