Till Rohrmann created FLINK-1736:
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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 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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