tsne) is an algorithm
for dimensionality reduction that is well-suited to visualizing high-dimensional
data. The name stands for t-distributed Stochastic
Neighbor Embedding. The idea is to embed high-dimensional points in
low dimensions in a way that respects similarities between points.
Nearby points in the high-dimensional space correspond to nearby embedded
low-dimensional points, and distant points in high-dimensional space
correspond to distant embedded low-dimensional points. (Generally,
it is impossible to match distances exactly between high-dimensional
and low-dimensional spaces.)
tsne function creates
a set of low-dimensional points from high-dimensional data. Typically,
you visualize the low-dimensional points to see natural clusters in
the original high-dimensional data.
The algorithm takes the following general steps to embed the data in low dimensions.
Calculate the pairwise distances between the high-dimensional points.
Create a standard deviation σi for each high-dimensional point i so that the perplexity of each point is at a predetermined level. For the definition of perplexity, see Compute Distances, Gaussian Variances, and Similarities.
Calculate the similarity matrix. This is the joint probability distribution of X, defined by Equation 1.
Create an initial set of low-dimensional points.
Iteratively update the low-dimensional points to minimize the Kullback-Leibler divergence between a Gaussian distribution in the high-dimensional space and a t distribution in the low-dimensional space. This optimization procedure is the most time-consuming part of the algorithm.
See van der Maaten and Hinton .
The basic t-SNE algorithm performs the following steps.
tsne first removes each row of the input
data X that contains any
NaN values. Then, if the
X by subtracting the mean of each column, and scales X by dividing
its columns by their standard deviations.
The original authors van der Maaten and Hinton  recommend reducing the original data X to
a lower-dimensional version using Principal Component Analysis (PCA). You can set the
pair to the number of dimensions you like, perhaps 50. To exercise
more control over this step, preprocess the data using the
After the preprocessing,
the distance d(xi,xj) between
each pair of points xi and xj in
X. You can choose various distance metrics using the
Distance name-value pair.
tsne uses the standard Euclidean
tsne uses the square of the distance
metric in its subsequent calculations.
Then for each row i of X,
a standard deviation σi so
that the perplexity of row i is
equal to the
pair. The perplexity is defined in terms of a model Gaussian distribution
as follows. As van der Maaten and Hinton  describe, “The similarity of datapoint xj to
datapoint xi is the conditional
that xi would pick xj as
its neighbor if neighbors were picked in proportion to their probability
density under a Gaussian centered at xi.
For nearby datapoints, is
relatively high, whereas for widely separated datapoints, will
be almost infinitesimal (for reasonable values of the variance of
the Gaussian, σi).”
Define the conditional probability of j given i as
Then define the joint probability pij by symmetrizing the conditional probabilities:
where N is the number of rows of X.
The distributions still do not have their standard deviations σi defined
in terms of the
pair. Let Pi represents
the conditional probability distribution over all other datapoints
given datapoint xi. The
perplexity of the distribution is
where H(Pi) is the Shannon entropy of Pi:
The perplexity measures the effective number of neighbors of
tsne performs a binary
search over the σi to
achieve a fixed perplexity for each point i.
To embed the points in X into a low-dimensional space,
tsne attempts to minimize the
Kullback-Leibler divergence between the model Gaussian distribution
of the points in X and a Student t distribution
of points Y in the low-dimensional space.
The minimization procedure begins with an initial set of points
tsne create the points by default as random
Gaussian-distributed points. You can also create these points yourself
and include them in the
'InitialY' name-value pair
tsne then calculates
the similarities between each pair of points in Y.
The probability model qij of the distribution of the distances between points yi and yj is
Using this definition and the model of distances in X given by Equation 1, the Kullback-Liebler divergence between the joint distribution P and Q is
For consequences of this definition, see Helpful Nonlinear Distortion.
To minimize the Kullback-Leibler divergence, the
uses a modified gradient descent procedure. The gradient with respect
to the points in Y of the divergence is
where the normalization term
The modified gradient descent algorithm uses a few tuning parameters to attempt to reach a good local minimum.
'Exaggeration' — During
the first 99 gradient descent steps,
the probabilities pij from Equation 1 by the exaggeration
value. This step tends to create more space between clusters in the
adaptive learning to improve the convergence of the gradient descent
iterations. The descent algorithm has iterative steps that are a linear
combination of the previous step in the descent and the current gradient.
a multiplier of the current gradient for the linear combination. For
details, see Jacobs .
To speed the t-SNE algorithm and to cut down on its memory usage,
an approximate optimization scheme. The Barnes-Hut algorithm groups
nearby points together to lower the complexity and memory usage of
the t-SNE optimization step. The Barnes-Hut algorithm is an approximate
optimizer, not an exact optimizer. There is a nonnegative tuning parameter
Theta that effects a tradeoff
between speed and accuracy. Larger values of
faster but less accurate optimization results. The algorithm is relatively
'Theta' values in the range (0.2,0.8).
The Barnes-Hut algorithm groups nearby points in the low-dimensional space, and performs an approximate gradient descent based on these groups. The idea, originally used in astrophysics, is that the gradient is similar for nearby points, so the computations can be simplified.
See van der Maaten .
Because t-SNE often separates data clusters well, it can seem that t-SNE can classify new data points. However, t-SNE cannot classify new points. The t-SNE embedding is a nonlinear map that is data-dependent. To embed a new point in the low-dimensional space, you cannot use the previous embedding as a map. Instead, run the entire algorithm again.
t-SNE can take a good deal of time to process data. If you have N data points in D dimensions that you want to map to Y dimensions, then
Exact t-SNE takes of order D*N2 operations.
Barnes-Hut t-SNE takes of order D*Nlog(N)*exp(dimension(Y)) operations.
So for large data sets, where N is greater than 1000 or so, and where the embedding dimension Y is 2 or 3, the Barnes-Hut algorithm can be faster than the exact algorithm.
T-SNE maps high-dimensional distances to distorted low-dimensional
analogues. Because of the fatter tail of the Student t distribution
in the low-dimensional space,
tsne often moves
close points closer together, and moves far points farther apart than
in the high-dimensional space, as illustrated in the following figure.
The figure shows both Gaussian and Student t distributions
at the points where the densities are at 0.25 and 0.025. The Gaussian
density relates to high-dimensional distances, and the t density
relates to low-dimensional distances. The t density
corresponds to close points being closer, and far points being farther,
compared to the Gaussian density.
t = linspace(0,5); y1 = normpdf(t,0,1); y2 = tpdf(t,1); plot(t,y1,'k',t,y2,'r') hold on x1 = fzero(@(x)normpdf(x,0,1)-0.25,[0,2]); x2 = fzero(@(x)tpdf(x,1)-0.25,[0,2]); z1 = fzero(@(x)normpdf(x,0,1)-0.025,[0,5]); z2 = fzero(@(x)tpdf(x,1)-0.025,[0,5]); plot([0,x1],[0.25,0.25],'k-.') plot([0,z2],[0.025,0.025],'k-.') plot([x1,x1],[0,0.25],'g-',[x2,x2],[0,0.25],'g-') plot([z1,z1],[0,0.025],'g-',[z2,z2],[0,0.025],'g-') text(1.1,.25,'Close points are closer in low-D') text(2.4,.05,'Far points are farther in low-D') legend('Gaussian(0,1)','Student t (df = 1)') xlabel('x') ylabel('Density') title('Density of Gaussian(0,1) and Student t (df = 1)') hold off
This distortion is helpful when it applies. It does not apply
in cases such as when the Gaussian variance is high, which lowers
the Gaussian peak and flattens the distribution. In such a case,
move close points farther apart than in the original space. To achieve
a helpful distortion,
'Verbose' name-value pair
pair so the reported range of variances is not too far from
and the mean variance is near
If you can achieve this range of variances, then the diagram
applies, and the
tsne distortion is helpful.
For effective ways to tune
tsne, see Wattenberg, Viégas and Johnson
 van der Maaten, Laurens, and Geoffrey Hinton. Visualizing Data using t-SNE. J. Machine Learning Research 9, 2008, pp. 2579–2605.
 van der Maaten, Laurens. Barnes-Hut-SNE. arXiv:1301.3342 [cs.LG], 2013.
 Jacobs, Robert A. Increased rates of convergence through learning rate adaptation. Neural Networks 1.4, 1988, pp. 295–307.
 Wattenberg, Martin, Fernanda Viégas, and Ian Johnson.
How to Use t-SNE Effectively. Distill, 2016. Available at
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