T-SNE visualisation of word embeddings generated using 19th century literature

T-SNE embeddings of MNIST dataset

t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis,^[1] where Laurens van der Maaten proposed the t-distributed variant.^[2] It is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.

The t-SNE algorithm comprises two main stages. First, t-SNE constructs a probability distribution over pairs of high-dimensional objects in such a way that similar objects are assigned a higher probability while dissimilar points are assigned a lower probability. Second, t-SNE defines a similar probability distribution over the points in the low-dimensional map, and it minimizes the Kullback–Leibler divergence (KL divergence) between the two distributions with respect to the locations of the points in the map. While the original algorithm uses the Euclidean distance between objects as the base of its similarity metric, this can be changed as appropriate. A Riemannian variant is UMAP.

t-SNE has been used for visualization in a wide range of applications, including genomics, computer security research,^[3] natural language processing, music analysis,^[4] cancer research,^[5] bioinformatics,^[6] geological domain interpretation,^[7][8][9] and biomedical signal processing.^[10]

While t-SNE plots often seem to display clusters, the visual clusters can be influenced strongly by the chosen parameterization and therefore a good understanding of the parameters for t-SNE is necessary. Such "clusters" can be shown to even appear in non-clustered data,^[11] and thus may be false findings. Interactive exploration may thus be necessary to choose parameters and validate results.^[12][13] It has been demonstrated that t-SNE is often able to recover well-separated clusters, and with special parameter choices, approximates a simple form of spectral clustering.^[14]

For a data set with n elements, t-SNE runs in O(n²) time and requires O(n²) space.^[15]

Details

Given a set of ${\displaystyle N}$ high-dimensional objects ${\displaystyle \mathbf {x} _{1},\dots ,\mathbf {x} _{N}}$, t-SNE first computes probabilities ${\displaystyle p_{ij}}$ that are proportional to the similarity of objects ${\displaystyle \mathbf {x} _{i}}$ and ${\displaystyle \mathbf {x} _{j}}$, as follows.

For ${\displaystyle i\neq j}$, define

${\displaystyle p_{j\mid i}={\frac {\exp(-\lVert \mathbf {x} _{i}-\mathbf {x} _{j}\rVert ^{2}/2\sigma _{i}^{2})}{\sum _{k\neq i}\exp(-\lVert \mathbf {x} _{i}-\mathbf {x} _{k}\rVert ^{2}/2\sigma _{i}^{2})}}}$

and set ${\displaystyle p_{i\mid i}=0}$. Note the above denominator ensures ${\displaystyle \sum _{j}p_{j\mid i}=1}$ for all ${\displaystyle i}$.

As Van der Maaten and Hinton explained: "The similarity of datapoint ${\displaystyle x_{j}}$ to datapoint ${\displaystyle x_{i}}$ is the conditional probability, ${\displaystyle p_{j|i}}$, that ${\displaystyle x_{i}}$ would pick ${\displaystyle x_{j}}$ as its neighbor if neighbors were picked in proportion to their probability density under a Gaussian centered at ${\displaystyle x_{i}}$."^[2]

Now define

${\displaystyle p_{ij}={\frac {p_{j\mid i}+p_{i\mid j}}{2N}}}$

This is motivated because ${\displaystyle p_{i}}$ and ${\displaystyle p_{j}}$ from the N samples are estimated as 1/N, so the conditional probability can be written as ${\displaystyle p_{i\mid j}=Np_{ij}}$ and ${\displaystyle p_{j\mid i}=Np_{ji}}$ . Since ${\displaystyle p_{ij}=p_{ji}}$, you can obtain previous formula.

Also note that ${\displaystyle p_{ii}=0}$ and ${\displaystyle \sum _{i,j}p_{ij}=1}$.

The bandwidth of the Gaussian kernels ${\displaystyle \sigma _{i}}$ is set in such a way that the entropy of the conditional distribution equals a predefined entropy using the bisection method. As a result, the bandwidth is adapted to the density of the data: smaller values of ${\displaystyle \sigma _{i}}$ are used in denser parts of the data space.

Since the Gaussian kernel uses the Euclidean distance ${\displaystyle \lVert x_{i}-x_{j}\rVert }$, it is affected by the curse of dimensionality, and in high dimensional data when distances lose the ability to discriminate, the ${\displaystyle p_{ij}}$ become too similar (asymptotically, they would converge to a constant). It has been proposed to adjust the distances with a power transform, based on the intrinsic dimension of each point, to alleviate this.^[16]

t-SNE aims to learn a ${\displaystyle d}$-dimensional map ${\displaystyle \mathbf {y} _{1},\dots ,\mathbf {y} _{N}}$ (with ${\displaystyle \mathbf {y} _{i}\in \mathbb {R} ^{d}}$ and ${\displaystyle d}$ typically chosen as 2 or 3) that reflects the similarities ${\displaystyle p_{ij}}$ as well as possible. To this end, it measures similarities ${\displaystyle q_{ij}}$ between two points in the map ${\displaystyle \mathbf {y} _{i}}$ and ${\displaystyle \mathbf {y} _{j}}$, using a very similar approach. Specifically, for ${\displaystyle i\neq j}$, define ${\displaystyle q_{ij}}$ as

${\displaystyle q_{ij}={\frac {(1+\lVert \mathbf {y} _{i}-\mathbf {y} _{j}\rVert ^{2})^{-1}}{\sum _{k}\sum _{l\neq k}(1+\lVert \mathbf {y} _{k}-\mathbf {y} _{l}\rVert ^{2})^{-1}}}}$

and set ${\displaystyle q_{ii}=0}$. Herein a heavy-tailed Student t-distribution (with one-degree of freedom, which is the same as a Cauchy distribution) is used to measure similarities between low-dimensional points in order to allow dissimilar objects to be modeled far apart in the map.

The locations of the points ${\displaystyle \mathbf {y} _{i}}$ in the map are determined by minimizing the (non-symmetric) Kullback–Leibler divergence of the distribution ${\displaystyle P}$ from the distribution ${\displaystyle Q}$, that is:

${\displaystyle \mathrm {KL} \left(P\parallel Q\right)=\sum _{i\neq j}p_{ij}\log {\frac {p_{ij}}{q_{ij}}}}$

The minimization of the Kullback–Leibler divergence with respect to the points ${\displaystyle \mathbf {y} _{i}}$ is performed using gradient descent. The result of this optimization is a map that reflects the similarities between the high-dimensional inputs.

Software

• The R package Rtsne implements t-SNE in R.
• ELKI contains tSNE, also with Barnes-Hut approximation
• scikit-learn, a popular machine learning library in Python implements t-SNE with both exact solutions and the Barnes-Hut approximation.
• Tensorboard, the visualization kit associated with TensorFlow, also implements t-SNE (online version)

References

External links

• Visualizing Data Using t-SNE, Google Tech Talk about t-SNE
• Implementations of t-SNE in various languages, A link collection maintained by Laurens van der Maaten