[SPARK-7334] Implement RandomProjection for Dimensionality Reduction

• Type: New Feature
• Status: Closed
• Priority: Minor
• Resolution: Duplicate
• Component/s: MLlib
• Assignee: Unassigned
• Reporter: Sebastian Alfers
• Created: 04/May/15 08:21
• Updated: 24/Oct/16 20:00
• Resolved: 24/Oct/16 19:59

Description

Implement RandomProjection (RP) for dimensionality reduction

RP is a popular approach to reduce the amount of data while preserving a reasonable amount of information (pairwise distance) of you data [1][2]

• [1] http://www.yaroslavvb.com/papers/achlioptas-database.pdf
• [2] http://people.inf.elte.hu/fekete/algoritmusok_msc/dimenzio_csokkentes/randon_projection_kdd.pdf

I compared different implementations of that algorithm:

• https://github.com/sebastian-alfers/random-projection-python

Issue Links

• Is contained by: SPARK-5992 Locality Sensitive Hashing (LSH)
• links to: [Github] Pull Request #6613 (sebastian-alfers)