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      Additive Feature Hashing

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          Abstract

          The hashing trick is a machine learning technique used to encode categorical features into a numerical vector representation of pre-defined fixed length. It works by using the categorical hash values as vector indices, and updating the vector values at those indices. Here we discuss a different approach based on additive-hashing and the "almost orthogonal" property of high-dimensional random vectors. That is, we show that additive feature hashing can be performed directly by adding the hash values and converting them into high-dimensional numerical vectors. We show that the performance of additive feature hashing is similar to the hashing trick, and we illustrate the results numerically using synthetic, language recognition, and SMS spam detection data.

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          Author and article information

          Journal
          07 February 2021
          Article
          2102.03943

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          11 pages 3 figures
          cs.LG

          Artificial intelligence

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