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Feature hashing

In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values as indices directly (after a modulo…

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Map overview Semantic statistics

Feature hashing

Nodes43
Edges42
Triples8
Avg. degree1.95
Density0.046512
Components1

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Feature hashing

Top relations

related to Feature hashing (Weinberger et al. 2009) · 5
Feature hashing → Finally, First, Next, The, Weinberger
has application · 3
Feature hashing → Dredze, Ganchev, Weinberger

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Important terminology

displaystyle feature hashing phi hash mathbb vector function zeta features tokens words set learning t' matrix trick machine using use

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Feature hashinghas applicationGanchev0.60section
Feature hashinghas applicationDredze0.60section
Feature hashinghas applicationWeinberger0.60section
Feature hashingrelated to Feature hashing (Weinberger et al. 2009)The0.60section
Feature hashingrelated to Feature hashing (Weinberger et al. 2009)Weinberger0.60section
Feature hashingrelated to Feature hashing (Weinberger et al. 2009)First0.60section
Feature hashingrelated to Feature hashing (Weinberger et al. 2009)Next0.60section
Feature hashingrelated to Feature hashing (Weinberger et al. 2009)Finally0.60section

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    Min side: 3
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