T-distributed stochastic neighbor embedding

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T-distributed stochastic neighbor embedding (t-SNE) is a machine learning algorithm for dimensionality reduction developed by Geoffrey Hinton and Laurens van der Maaten. It is particularly well-suited for the visualization of high-dimensional datasets.

Overview[edit | edit source]

t-SNE is a non-linear technique primarily used for data visualization. It converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. The algorithm is an extension of Stochastic Neighbor Embedding (SNE) and aims to address the crowding problem inherent in SNE.

Algorithm[edit | edit source]

The t-SNE algorithm consists of two main stages: 1. **Computing pairwise similarities**: In the high-dimensional space, the similarity between two points is measured using a Gaussian distribution. In the low-dimensional space, a Student's t-distribution is used to measure similarity. 2. **Minimizing the Kullback-Leibler divergence**: The algorithm iteratively adjusts the positions of points in the low-dimensional space to minimize the divergence between the high-dimensional and low-dimensional similarity distributions.

Applications[edit | edit source]

t-SNE is widely used in various fields such as:

Advantages and Disadvantages[edit | edit source]

Advantages[edit | edit source]

  • Effective in capturing the local structure of the data.
  • Produces visually interpretable results.

Disadvantages[edit | edit source]

  • Computationally intensive, especially for large datasets.
  • The results can be sensitive to the choice of hyperparameters such as perplexity.

Related Techniques[edit | edit source]

See Also[edit | edit source]

References[edit | edit source]

External Links[edit | edit source]


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Contributors: Prab R. Tumpati, MD