Publications by years in reversed chronological order

Example: a mock contribution

This page is a reworking of the original t-SNE article using the
Computo template. It aims to help authors submitting to the journal by
using some advanced formatting features.

Visualizing Data using t-SNE: practical Computo example

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:stochastic 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
different scales. This is particularly important for
high-dimensional data that lie on several different,
but related, low-dimensional manifolds, such as
images of objects from multiple classes seen from
multiple viewpoints. For visualizing the structure
of very large data sets, we show how t-SNE can use
random walks on neighborhood graphs to allow the
implicit structure of all the data to influence the
way in which a subset of the data is displayed. We
illustrate the performance of t-SNE on a wide
variety of data sets and compare it with many other
non-parametric visualization techniques, including
Sammon mapping, Isomap, and Locally Linear
Embedding. The visualization produced by t-SNE are
significantly better than those produced by other
techniques on almost all of the data sets.