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From "Till Rohrmann (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (FLINK-1736) Add CountVectorizer to machine learning library
Date Tue, 07 Jun 2016 13:56:21 GMT

     [ https://issues.apache.org/jira/browse/FLINK-1736?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Till Rohrmann updated FLINK-1736:
---------------------------------
    Assignee: ROSHANI NAGMOTE  (was: Alexander Alexandrov)

> Add CountVectorizer to machine learning library
> -----------------------------------------------
>
>                 Key: FLINK-1736
>                 URL: https://issues.apache.org/jira/browse/FLINK-1736
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: ROSHANI NAGMOTE
>              Labels: ML, Starter
>
> A {{CountVectorizer}} feature extractor [1] assigns each occurring word in a corpus an
unique identifier. With this mapping it can vectorize models such as bag of words or ngrams
in a efficient way. The unique identifier assigned to a word acts as the index of a vector.
The number of word occurrences is represented as a vector value at a specific index. 
> The advantage of the {{CountVectorizer}} compared to the FeatureHasher is that the mapping
of words to indices can be obtained which makes it easier to understand the resulting feature
vectors.
> The {{CountVectorizer}} could be generalized to support arbitrary feature values.
> The {{CountVectorizer}} should be implemented as a {{Transfomer}}.
> Resources:
> [1] [http://scikit-learn.org/stable/modules/feature_extraction.html#common-vectorizer-usage]



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