Project details for ExtRESCAL

Logo ExtRESCAL 0.5.1

by nzhiltsov - July 3, 2013, 09:38:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Ext-RESCAL is a memory efficient implementation of RESCAL, a state-of-the-art algorithm for DEDICOM-like tensor factorization. Ext-RESCAL is written in Python and relies on the SciPy Sparse module.

  • 3-D sparse tensor factorization [1];
  • Joint 3-D sparse tensor and 2-D sparse matrix factorization (extended version) [2];
  • The implementation provably scales well to the domains with millions of nodes on the affordable hardware;
  • Handy input format;

[1] M. Nickel, V. Tresp, H. Kriegel. A Three-way Model for Collective Learning on Multi-relational Data // Proceedings of the 28th International Conference on Machine Learning (ICML'2011). - 2011.

[2] M. Nickel, V. Tresp, H. Kriegel. Factorizing YAGO: Scalable Machine Learning for Linked Data // Proceedings of the 21st international conference on World Wide Web (WWW'2012). - 2012.

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Platform Independent
Data Formats: Csv
Tags: Tensor, Factorization
Archive: download here

Other available revisons

Version Changelog Date
  • Improve (speed up) initialization of A by summation
January 20, 2015, 00:35:15
  • Grealy improve the memory consumption for all scripts after refactoring to using csr_matrix
  • Fix the eigenvalue initialization
October 11, 2014, 17:08:01
  • Make the extended algorigthm output fixed (by replacing random initialization)
  • Add handling of float values in the extended task
  • Add the util for matrix pseudo inversion
  • Switch to Apache License 2.0
March 21, 2014, 16:22:58

Initial Announcement on

July 3, 2013, 09:38:02


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