DB Tsai created SPARK-2272:
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Summary: Feature scaling which standardizes the range of
independent variables or features of data.
Key: SPARK-2272
URL: https://issues.apache.org/jira/browse/SPARK-2272
Project: Spark
Issue Type: New Feature
Components: MLlib
Reporter: DB Tsai
Feature scaling is a method used to standardize the range of independent
variables or features of data. In data processing, it is also known as data
normalization and is generally performed during the data preprocessing step.
In this work, a trait called `VectorTransformer` is defined for generic
transformation of a vector. It contains two methods, `apply` which applies
transformation on a vector and `unapply` which applies inverse transformation
on a vector.
There are three concrete implementations of `VectorTransformer`, and they all
can be easily extended with PMML transformation support.
1) `VectorStandardizer` - Standardises a vector given the mean and variance.
Since the standardization will densify the output, the output is always in
dense vector format.
2) `VectorRescaler` - Rescales a vector into target range specified by a tuple
of two double values or two vectors as new target minimum and maximum. Since
the rescaling will substrate the minimum of each column first, the output will
always be in dense vector regardless of input vector type.
3) `VectorDivider` - Transforms a vector by dividing a constant or diving a
vector with element by element basis. This transformation will preserve the
type of input vector without densifying the result.
Utility helper methods are implemented for taking an input of RDD[Vector], and
then transformed RDD[Vector] and transformer are returned for dividing,
rescaling, normalization, and standardization.
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