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Cosine similarity

Technique

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About Cosine similarity · from Wikipedia

In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval For example, two proportional vectors have a cosine similarity of +1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of −1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in.

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  • thealgorithm Jan 2025
    Text and data: cosine similarity and edit distance. Machine learning: Pegasos, a fast method for training support-vector machines.

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Everything on this page is read from triples in the knowledge graph. These are the 3 it states about Cosine similarity, and 1 more its ontology implies; the console runs SPARQL over the whole graph, here in your browser.

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id:CosineSimilarity
    a cj:Technique ;
    rdfs:label "Cosine similarity" ;
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thealgorithm