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From "Till Rohrmann (JIRA)" <j...@apache.org>
Subject [jira] [Created] (FLINK-1736) Add CountVectorizer to machine learning library
Date Wed, 18 Mar 2015 15:07:39 GMT
Till Rohrmann created FLINK-1736:

             Summary: Add CountVectorizer to machine learning library
                 Key: FLINK-1736
                 URL: https://issues.apache.org/jira/browse/FLINK-1736
             Project: Flink
          Issue Type: Improvement
          Components: Machine Learning Library
            Reporter: Till Rohrmann

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

The {{CountVectorizer}} could be generalized to support arbitrary feature values.

The {{CountVectorizer}} should be implemented as a {{Transfomer}}.

[1] [http://scikit-learn.org/stable/modules/feature_extraction.html#common-vectorizer-usage]

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