Revise docs, include refs

LICENSE.txt

@@ -1,4 +1,4 @@
-Copyright (c) 2011-2015 Marinka Zitnik and Blaz Zupan.
+Copyright (c) 2011-2016
 
 All rights reserved.
 

README.rst

@@ -50,8 +50,8 @@ To install for all users on Unix/Linux::
 For more detailed installation instructions,
 see the web page http://nimfa.biolab.si
 
-Usage
------
+Use
+---
 
 Run alternating least squares nonnegative matrix factorization with projected gradients and Random Vcol initialization algorithm on medulloblastoma gene expression data::
 
@@ -69,8 +69,8 @@ Run alternating least squares nonnegative matrix factorization with projected gr
     Sparseness, W: 0.7297, H: 0.8796
 
 
-Citing
-------
+Cite
+----
 
 ::
 
@@ -87,7 +87,7 @@ Citing
 License
 -------
 
-nimfa - A Python Library for Nonnegative Matrix Factorization Techniques
+Nimfa - A Python Library for Nonnegative Matrix Factorization Techniques
 Copyright (C) 2011-2016
 
 This program is free software: you can redistribute it and/or modify

docs/source/index.rst

@@ -164,87 +164,147 @@ Standard NMF - Euclidean update equations and fixed initialization (passed matri
 .. literalinclude:: /code/usage6.py
 
 
+****
+
+We an IPython notebook showing how to `use Nimfa to analyze breast cancer transcriptome data sets`_
+from The International Cancer Genome Consortium (`ICGC`_).
+
+The analysis of cancer data using Nimfa was highlighted in the `ACM XRDS magazine`_.
+
+.. _use Nimfa to analyze breast cancer transcriptome data sets: http://nbviewer.ipython.org/github/marinkaz/nimfa-ipynb/blob/master/ICGC%20and%20Nimfa.ipynb
+.. _ICGC: https://dcc.icgc.org
+.. _ACM XRDS magazine: http://dl.acm.org/citation.cfm?id=2809623.2788526&coll=portal&dl=ACM
+
+
 ##########
 References
 ##########
 
-.. [Schmidt2009] Mikkel N. Schmidt, Ole Winther, and Lars K. Hansen. Bayesian non-negative matrix factorization. In Proceedings of the 9th International Conference on Independent Component Analysis and Signal Separation, pages 540-547, Paraty, Brazil, 2009.
+.. [Schmidt2009] Mikkel N. Schmidt, Ole Winther, and Lars K. Hansen. Bayesian non-negative
+matrix factorization. In Proceedings of the 9th International Conference on Independent
+Component Analysis and Signal Separation, pages 540-547, Paraty, Brazil, 2009.
 
-.. [Zhang2007] Zhongyuan Zhang, Tao Li, Chris H. Q. Ding and Xiangsun Zhang. Binary Matrix Factorization with applications. In Proceedings of 7th IEEE International Conference on Data Mining, pages 391-400, Omaha, USA, 2007.
+.. [Zhang2007] Zhongyuan Zhang, Tao Li, Chris H. Q. Ding and Xiangsun Zhang. Binary Matrix
+Factorization with applications. In Proceedings of 7th IEEE International Conference on Data
+Mining, pages 391-400, Omaha, USA, 2007.
 
-.. [Wang2004] Yuan Wang, Yunde Jia, Changbo Hu and Matthew Turk. Fisher non-negative matrix factorization for learning local features. In Proceedings of the 6th Asian Conference on Computer Vision, pages 27-30, Jeju, Korea, 2004.  
+.. [Wang2004] Yuan Wang, Yunde Jia, Changbo Hu and Matthew Turk. Fisher non-negative matrix
+factorization for learning local features. In Proceedings of the 6th Asian Conference on
+Computer Vision, pages 27-30, Jeju, Korea, 2004.
 
-.. [Li2001] Stan Z. Li, Xinwen Huo, Hongjiang Zhang and Qian S. Cheng. Learning spatially localized, parts-based representation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 207-212, Kauai, USA, 2001.  
+.. [Li2001] Stan Z. Li, Xinwen Huo, Hongjiang Zhang and Qian S. Cheng. Learning spatially
+localized, parts-based representation. In Proceedings of the IEEE Conference on Computer Vision
+and Pattern Recognition, pages 207-212, Kauai, USA, 2001.
 
-.. [Lin2007] Chin J. Lin. Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19(10): 2756-2779, 2007. 
+.. [Lin2007] Chin J. Lin. Projected gradient methods for nonnegative matrix factorization.
+Neural Computation, 19(10): 2756-2779, 2007.
 
-.. [Lee2001] Daniel D. Lee and H. Sebastian Seung. Algorithms for non-negative matrix factorization. In Proceedings of the Neural Information Processing Systems, pages 556-562, Vancouver, Canada, 2001. 
+.. [Lee2001] Daniel D. Lee and H. Sebastian Seung. Algorithms for non-negative matrix
+factorization. In Proceedings of the Neural Information Processing Systems, pages 556-562,
+Vancouver, Canada, 2001.
 
-.. [Lee1999] Daniel D. Lee and H. Sebastian Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755): 788-791, 1999. 
+.. [Lee1999] Daniel D. Lee and H. Sebastian Seung. Learning the parts of objects by non-negative
+ matrix factorization. Nature, 401(6755): 788-791, 1999.
 
-.. [Brunet2004] Jean-P. Brunet, Pablo Tamayo, Todd R. Golub and Jill P. Mesirov. Metagenes and molecular pattern discovery using matrix factorization. In Proceedings of the National Academy of Sciences of the USA, 101(12): 4164-4169, 2004.
+.. [Brunet2004] Jean-P. Brunet, Pablo Tamayo, Todd R. Golub and Jill P. Mesirov. Metagenes and
+molecular pattern discovery using matrix factorization. In Proceedings of the National Academy
+of Sciences of the USA, 101(12): 4164-4169, 2004.
 
-.. [Montano2006] Alberto Pascual-Montano, J. M. Carazo, Kieko Kochi, Dietrich Lehmann and Roberto D. Pascual-Marqui. Nonsmooth nonnegative matrix factorization (nsnmf). In IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(3): 403-415, 2006. 
+.. [Montano2006] Alberto Pascual-Montano, J. M. Carazo, Kieko Kochi, Dietrich Lehmann and
+Roberto D. Pascual-Marqui. Nonsmooth nonnegative matrix factorization (nsnmf). In IEEE
+Transactions on Pattern Analysis and Machine Intelligence, 28(3): 403-415, 2006.
 
-.. [Laurberg2008] Hans Laurberg, Mads G. Christensen, Mark D. Plumbley, Lars K. Hansen and Soren H. Jensen. Theorems on positive data: on the uniqueness of NMF. Computational Intelligence and Neuroscience, doi: 10.1155/2008/764206, 2008. 
+.. [Laurberg2008] Hans Laurberg, Mads G. Christensen, Mark D. Plumbley, Lars K. Hansen and Soren
+ H. Jensen. Theorems on positive data: on the uniqueness of NMF. Computational Intelligence and Neuroscience, doi: 10.1155/2008/764206, 2008.
 
-.. [Hansen2008] Lars K. Hansen. Generalization in high-dimensional factor models. Web: http://www.stanford.edu/group/mmds/slides2008/hansen.pdf, 2008. 
+.. [Hansen2008] Lars K. Hansen. Generalization in high-dimensional factor models. Web:
+http://www.stanford.edu/group/mmds/slides2008/hansen.pdf, 2008.
 
-.. [Dueck2005] Delbert Dueck, Quaid D. Morris and Brendan J. Frey. Multi-way clustering of microarray data using probabilistic sparse matrix factorization. Bioinformatics, 21(Suppl 1): 144-151, 2005. 
+.. [Dueck2005] Delbert Dueck, Quaid D. Morris and Brendan J. Frey. Multi-way clustering of
+microarray data using probabilistic sparse matrix factorization. Bioinformatics, 21(Suppl 1):
+144-151, 2005.
 
-.. [Dueck2004] Delbert Dueck and Brendan J. Frey. Probabilistic sparse matrix factorization. University of Toronto Technical Report PSI-2004-23, Probabilistic and Statistical Inference Group, University of Toronto, 2004. 
+.. [Dueck2004] Delbert Dueck and Brendan J. Frey. Probabilistic sparse matrix factorization.
+University of Toronto Technical Report PSI-2004-23, Probabilistic and Statistical Inference
+Group, University of Toronto, 2004.
 
-.. [Srebro2001] Nathan Srebro and Tommi Jaakkola. Sparse matrix Factorization of gene expression data. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 2001. 
+.. [Srebro2001] Nathan Srebro and Tommi Jaakkola. Sparse matrix Factorization of gene expression
+ data. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 2001.
 
-.. [Li2007] Huan Li, Yu Sun and Ming Zhan. The discovery of transcriptional modules by a two-stage matrix decomposition approach. Bioinformatics, 23(4): 473-479, 2007. 
+.. [Li2007] Huan Li, Yu Sun and Ming Zhan. The discovery of transcriptional modules by a
+two-stage matrix decomposition approach. Bioinformatics, 23(4): 473-479, 2007.
 
-.. [Park2007] Hyuonsoo Kim and Haesun Park. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics, 23(12): 1495-1502, 2007. 
+.. [Park2007] Hyuonsoo Kim and Haesun Park. Sparse non-negative matrix factorizations via
+alternating non-negativity-constrained least squares for microarray data analysis.
+Bioinformatics, 23(12): 1495-1502, 2007.
 
-.. [Zhang2011] Shihua Zhang, Qingjiao Li and Xianghong J. Zhou. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics, 27(13): 401-409, 2011. 
+.. [Zhang2011] Shihua Zhang, Qingjiao Li and Xianghong J. Zhou. A novel computational framework
+for simultaneous integration of multiple types of genomic data to identify microRNA-gene
+regulatory modules. Bioinformatics, 27(13): 401-409, 2011.
 
-.. [Boutsidis2007] Christos Boutsidis and Efstratios Gallopoulos. SVD-based initialization: A head start for nonnegative matrix factorization. Pattern Recognition, 41(4): 1350-1362, 2008. 
+.. [Boutsidis2007] Christos Boutsidis and Efstratios Gallopoulos. SVD-based initialization: A
+head start for nonnegative matrix factorization. Pattern Recognition, 41(4): 1350-1362, 2008.
 
-.. [Albright2006] Russell Albright, Carl D. Meyer and Amy N. Langville. Algorithms, initializations, and convergence for the nonnegative matrix factorization. NCSU Technical Report Math 81706, NC State University, Releigh, USA, 2006. 
+.. [Albright2006] Russell Albright, Carl D. Meyer and Amy N. Langville. Algorithms,
+initializations, and convergence for the nonnegative matrix factorization. NCSU Technical Report
+ Math 81706, NC State University, Releigh, USA, 2006.
 
-.. [Hoyer2004] Patrik O. Hoyer. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5: 1457-1469, 2004. 
+.. [Hoyer2004] Patrik O. Hoyer. Non-negative matrix factorization with sparseness constraints.
+Journal of Machine Learning Research, 5: 1457-1469, 2004.
 
-.. [Hutchins2008] Lucie N. Hutchins, Sean P. Murphy, Priyam Singh and Joel H. Graber. Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics, 24(23): 2684-2690, 2008.
+.. [Hutchins2008] Lucie N. Hutchins, Sean P. Murphy, Priyam Singh and Joel H. Graber.
+Position-dependent motif characterization using non-negative matrix factorization.
+Bioinformatics, 24(23): 2684-2690, 2008.
 
-.. [Frigyesi2008] Attila Frigyesi and Mattias Hoglund. Non-negative matrix factorization for the analysis of complex gene expression data: identification of clinically relevant tumor subtypes. Cancer Informatics, 6: 275-292, 2008.
+.. [Frigyesi2008] Attila Frigyesi and Mattias Hoglund. Non-negative matrix factorization for the
+ analysis of complex gene expression data: identification of clinically relevant tumor subtypes.
+  Cancer Informatics, 6: 275-292, 2008.
 
-.. [FWang2008] Fei Wang, Tao Li, Changshui Zhang. Semi-Supervised Clustering via Matrix Factorization. SDM 2008, 1-12, 2008.  
+.. [FWang2008] Fei Wang, Tao Li, Changshui Zhang. Semi-Supervised Clustering via Matrix
+Factorization. SDM 2008, 1-12, 2008.
 
-.. [Schietgat2010] Leander Schietgat, Celine Vens, Jan Struyf, Hendrik Blockeel, Dragi Kocev and Saso Dzeroski. Predicting gene function using hierarchical multi-label decision tree ensembles. BMC Bioinformatics, 11(2), 2010.
+.. [Schietgat2010] Leander Schietgat, Celine Vens, Jan Struyf, Hendrik Blockeel, Dragi Kocev and
+ Saso Dzeroski. Predicting gene function using hierarchical multi-label decision tree ensembles.
+  BMC Bioinformatics, 11(2), 2010.
  
-.. [Schachtner2008] R. Schachtner, D. Lutter, P. Knollmueller, A. M. Tome, F. J. Theis, G. Schmitz, M. Stetter, P. Gomez Vilda and E. W. Lang. Knowledge-based gene expression classification via matrix factorization. Bioinformatics, 24(15): 1688-1697, 2008.
+.. [Schachtner2008] R. Schachtner, D. Lutter, P. Knollmueller, A. M. Tome, F. J. Theis, G.
+Schmitz, M. Stetter, P. Gomez Vilda and E. W. Lang. Knowledge-based gene expression 
+classification via matrix factorization. Bioinformatics, 24(15): 1688-1697, 2008.
 
+.. [Damle2014random] Anil Damle, Sun Yuekai . A geometric approach to archetypal analysis and
+non-negative matrix factorization. arXiv preprint arXiv:1405.4275, 2014.
 
-################
-Acknowledgements
-################
+.. [Benson2014scalable] Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich.
+Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny
+matrices. NIPS, 945-953, 2014.
+
+.. [Kumar2013fast] Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur. Fast conical hull
+algorithms for near-separable non-negative matrix factorization. ICML, 231-239, 2013.
+
+.. [Gillis2014fast] Gillis, Nicolas, and Stephen A. Vavasis. Fast and robust recursive
+algorithms for separable nonnegative matrix factorization. IEEE TPAMI, 36(4): 698-714, 2014.
 
-We would like to acknowledge support for this project from the Google Summer of Code 2011 program and 
-from the Slovenian Research Agency grants P2-0209, J2-9699, and L2-1112.
+.. [Tepper2015compressed] Mariano Tepper, Guillermo Sapiro. Compressed nonnegative matrix
+factorization is fast and accurate. IEEE TSP, 64(9): 2269-2283, 2016
 
-The nimfa - A Python Library for Nonnegative Matrix Factorization Techniques was part of the Google Summer of Code 2011. It is authored by `Marinka Zitnik`_ and `Blaz Zupan`_.
+.. [Kapralov2016fake] Michael, Kapralov, Vamsi Potluru, David Woodruff. How to fake multiply by a
+ Gaussian Matrix. ICML, 2101-2110. 2016.
 
-.. _Blaz Zupan: http://www.biolab.si/en/blaz/
-.. _Marinka Zitnik: http://helikoid.si
 
 ########
 Citation
 ########
 
 .. code-block:: none
 
-	@article{ZitnikZ12,
-	  author    = {Marinka Zitnik and Blaz Zupan},
-	  title     = {NIMFA: A Python Library for Nonnegative Matrix Factorization},
-	  journal   = {Journal of Machine Learning Research},
-	  volume    = {13},
-	  year      = {2012},
-	  pages     = {849-853},
-	}
+    @article{Zitnik2012,
+     title     = {Nimfa: A Python Library for Nonnegative Matrix Factorization},
+     author    = {Zitnik, Marinka and Zupan, Blaz},
+     journal   = {Journal of Machine Learning Research},
+     volume    = {13},
+     pages     = {849-853},
+     year      = {2012}
+   }
 
 	
 ##########
@@ -260,7 +320,7 @@ License
 #######
 
 nimfa - A Python Library for Nonnegative Matrix Factorization Techniques
-Copyright (C) 2011-2015 Marinka Zitnik and Blaz Zupan.
+Copyright (C) 2011-2016
 
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU General Public License as published by

nimfa/methods/factorization/nmf.py

@@ -4,10 +4,10 @@
 Nmf (``methods.factorization.nmf``)
 ###################################
 
-**Standard Nonnegative Matrix Factorization (NMF)** [Lee2001]_, [Lee1999]. 
+**Standard Nonnegative Matrix Factorization (NMF)** [Lee2001]_, [Lee1999]_.
 
 Based on Kullback-Leibler divergence, it uses simple multiplicative updates
-[Lee2001]_, [Lee1999], enhanced to avoid numerical underflow [Brunet2004]_.
+[Lee2001]_, [Lee1999]_, enhanced to avoid numerical underflow [Brunet2004]_.
 Based on Euclidean distance, it uses simple multiplicative updates [Lee2001]_.
 Different objective functions can be used, namely Euclidean distance, divergence
 or connectivity matrix convergence.

nimfa/methods/factorization/sepnmf.py

@@ -1,16 +1,18 @@
 
 """
-###################################
-Nmf (``methods.factorization.sepnmf``)
-###################################
+#########################################
+SepNmf (``methods.factorization.sepnmf``)
+#########################################
 
-**Separable Nonnegative Matrix Factorization (NMF)** [damle2014random]_, [benson2014scalable], [kumar2013fast], [gillis2014fast], [tepper2015compressed],
-[Kapralov2016fake]
+**Separable Nonnegative Matrix Factorization (SepNMF)** [Damle2014random]_, [Benson2014scalable]_,
+[Kumar2013fast]_, [Gillis2014fast]_, [Tepper2015compressed]_, [Kapralov2016fake]_
 
 Separable NMF was introduced by Donoho and Stodden (2003) and polynomial time algorithms were given by Arora et al 2012.
-Other algorithms such as XRAY [gillis2014fast], SPA [kumar2013fast] and more recently SC [tepper2015compressed and CG [Kapralov2016fake] have been proposed.
+Other algorithms such as XRAY [Gillis2014fast]_, SPA [Kumar2013fast]_ and more recently SC
+[Tepper2015compressed]_ and CG [Kapralov2016fake]_ have been proposed.
 
-Can be used for problems which satisfy the ``pure-pixel'' assumption which occurs in hyper-spectral imaging and  document analysis settings.
+SepNMF can be used for problems which satisfy the ``pure-pixel'' assumption which occurs in
+hyper-spectral imaging and document analysis settings.
 
 """
 try:

setup.py

@@ -7,7 +7,7 @@
 
 DISTNAME = 'nimfa'
 MAINTAINER = 'Marinka Zitnik'
-MAINTAINER_EMAIL = 'marinka.zitnik@fri.uni-lj.si'
+MAINTAINER_EMAIL = 'marinka@cs.stanford.edu'
 DESCRIPTION = 'A Python module for nonnegative matrix factorization'
 LONG_DESCRIPTION = open('README.rst').read()
 URL = 'http://nimfa.biolab.si'