[SPARK-6227] PCA and SVD for PySpark

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 1.2.1
• Fix Version/s: 2.2.0
• Component/s: MLlib, PySpark
• Assignee: Manoj Kumar
• Reporter: Julien Amelot
• Created: 09/Mar/15 15:50
• Updated: 08/Jul/17 13:20
• Resolved: 03/May/17 08:57

Description

The Dimensionality Reduction techniques are not available via Python (Scala + Java only).

• Principal component analysis (PCA)
• Singular value decomposition (SVD)

Doc:
http://spark.apache.org/docs/1.2.1/mllib-dimensionality-reduction.html

Issue Links

• depends upon: SPARK-6485 Add CoordinateMatrix/RowMatrix/IndexedRowMatrix in PySpark
• links to: [Github] Pull Request #7963 (MechCoder)
• links to: [Github] Pull Request #17621 (MLnick)