T-SNE Dimensionality Reduction

Table of contents

  1. Introduction
  2. How to use
  3. Behavior
  4. Method
  5. References

Introduction

T-SNE Dimensionality Reduction is a nonlinear dimensionality-reduction algorithm for visualizing high-dimensional spectra in two dimensions. It is useful for inspecting local grouping patterns, but its axes do not have direct physical meaning.

How to use

  1. Upload data and finish preprocessing if needed.
  2. Open Analytics Page.
  3. In Select Analytics Plot, choose T-SNE Dimensionality Reduction.
  4. Set t-SNE Perplexity.
  5. Set t-SNE Maximum number of iterations.

Behavior

SpectraGuru displays a two-dimensional t-SNE visualization of the selected spectra. Nearby points represent spectra that the algorithm placed close together in the embedding. If labels are available from Data Upload, they are used for coloring.

Method

t-SNE minimizes the divergence between neighbor probabilities in the original space and the two-dimensional embedding:

\[D_{KL}(P\Vert Q)=\sum_{i,j}p_{ij}\log\frac{p_{ij}}{q_{ij}}\]
Parameter Tunable or fixed Implementation
t-SNE Perplexity Tunable Slider from 1 to N-1; default 2
t-SNE Maximum number of iterations Tunable Slider 200-1000; default 500
Output dimensions Fixed 2D embedding
t-SNE implementation Fixed sklearn.manifold.TSNE

References

  1. van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579-2605. https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
  2. Scikit-learn Developers. TSNE. https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html