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Posted to dev@flink.apache.org by "Till Rohrmann (JIRA)" <ji...@apache.org> on 2015/03/18 15:54:38 UTC

[jira] [Created] (FLINK-1735) Add FeatureHasher to machine learning library

Till Rohrmann created FLINK-1735:
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             Summary: Add FeatureHasher to machine learning library
                 Key: FLINK-1735
                 URL: https://issues.apache.org/jira/browse/FLINK-1735
             Project: Flink
          Issue Type: Improvement
          Components: Machine Learning Library
            Reporter: Till Rohrmann


Using the hashing trick [1,2] is a common way to vectorize arbitrary feature values. The hash of the feature value is used to calculate its index for a vector entry. In order to mitigate possible collisions, a second hashing function is used to calculate the sign for the update value which is added to the vector entry. This way, it is likely that collision will simply cancel out.

A feature hasher would also be helpful for NLP problems where it could be used to vectorize bag of words or ngrams feature vectors.

Resources:
[1] [https://en.wikipedia.org/wiki/Feature_hashing]
[2] [http://scikit-learn.org/stable/modules/feature_extraction.html#feature-extraction]



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