T-distributed stochastic neighbor embedding: Difference between revisions

Revision as of 19:43, 17 August 2022

Revision as of 15:03, 3 November 2022

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: <math>\mathrm{KL}\left(P \parallel Q\right) = \sum_{i \neq j} p_{ij} \log \frac{p_{ij}}{q_{ij}}</math>

TheHI minimization of the Kullback–Leibler divergence with respect to the points <math>\mathbf{y}_i</math> is performed using [[gradient descent]].

The result of this optimization is a map that reflects the similarities between the high-dimensional inputs.