Corpus ID: 5855042

Visualizing Data using t-SNE

@inproceedings{Maaten2008VisualizingDU,
  title={Visualizing Data using t-SNE},
  author={Laurens van der Maaten and Geoffrey E. Hinton},
  year={2008}
}
  • Laurens van der Maaten, Geoffrey E. Hinton
  • Published 2008
  • Mathematics
  • 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. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 40 REFERENCES

    Stochastic Neighbor Embedding

    VIEW 10 EXCERPTS

    Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL

    A Nonlinear Mapping for Data Structure Analysis

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Learning Deep Architectures for AI

    • Yoshua Bengio
    • Computer Science
    • Foundations and Trends in Machine Learning
    • 2007
    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    On a Connection between Kernel PCA and Metric Multidimensional Scaling

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Multivariate Analysis

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL