Translations:Word Embeddings/8/en: Difference between revisions

Revision as of 22:01, 27 April 2026

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* '''Dimensionality''' — vectors are extremely high-dimensional (typically <math>V > 100{,}000</math>).
* '''No similarity''' — every pair of {{Term|one-hot encoding|one-hot}} vectors is equally distant: <math>\mathbf{e}_i^\top \mathbf{e}_j = 0</math> for <math>i \neq j</math>. "Cat" is as far from "dog" as it is from "democracy."

Dimensionality — vectors are extremely high-dimensional (typically $$ V > 100{,}000 $$ ).
No similarity — every pair of one-hot vectors is equally distant: $\mathbf{e}_i^\top \mathbf{e}_j = 0$ for $i \neq j$ . "Cat" is as far from "dog" as it is from "democracy."

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	* '''Dimensionality''' — vectors are extremely high-dimensional (typically <math>V > 100{,}000</math>).		* '''Dimensionality''' — vectors are extremely high-dimensional (typically <math>V > 100{,}000</math>).
	* '''No similarity''' — every pair of ~~{{Term\|~~one-hot ~~encoding\|one-hot}}~~ vectors is equally distant: <math>\mathbf{e}_i^\top \mathbf{e}_j = 0</math> for <math>i \neq j</math>. "Cat" is as far from "dog" as it is from "democracy."		* '''No similarity''' — every pair of one-hot vectors is equally distant: <math>\mathbf{e}_i^\top \mathbf{e}_j = 0</math> for <math>i \neq j</math>. "Cat" is as far from "dog" as it is from "democracy."