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SMR - Sparse matrix regression for coclustering


Coclustering is the tool of choice when only a smaller subset of variables are related to a specific grouping among some subjects. Hence, coclustering allows a select number of objects to have particular behavior on a select number of variables.



This SMR algorithm is a beta version which will allow you to build a coclustering just by selecting the number of coclusters. Be aware that coclustering is intrinsically difficult so problems with local minima may occur. Also be aware that the current algorithm works best with data that is fairly square and fairly discrete.


Download SMR


And some of the data used can be found at this page and the animal data here.


If you use the functions provided here, you may want to reference

  • E. E. Papalexakis, N. D. Sidiropoulos, and M. N. Garofalakis. Reviewer Profiling Using Sparse Matrix Regression.  1214-1219. 2010. 2010 IEEE International Conference on Data Mining Workshops.
  • E. E. Papalexakis and N. D. Sidiropoulos. Co-clustering as multilinear decomposition with sparse latent factors.  2011. Prague, Czech Republic. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing.
  • R. Bro, E. Acar, V. Papalexakis, N. D. Sidiropoulos, Coclustering, Journal of Chemometrics, 2012, 26, 256-263.




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