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

["Chesnay Schepler (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-1735?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Chesnay Schepler closed FLINK-1735.
-----------------------------------
    Resolution: Won't Fix

Closing since flink-ml is effectively frozen.

> Add FeatureHasher to machine learning library
> ---------------------------------------------
>
>                 Key: FLINK-1735
>                 URL: https://issues.apache.org/jira/browse/FLINK-1735
>             Project: Flink
>          Issue Type: New Feature
>          Components: Library / Machine Learning
>            Reporter: Till Rohrmann
>            Assignee: Felix Neutatz
>            Priority: Major
>              Labels: ML, pull-request-available
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> 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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