Analysis of financial data using non-negative matrix factorisation

We apply Non-negative Matrix Factorization (NMF) to the prob- lem of identifying underlying trends in stock market data. NMF is a recent and very successful tool for data analysis including image a ...

[1]  Bjarni Bödvarsson,et al.  Analysis of Dynamic PET Data , 2006 .

[2]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[3]  Nanning Zheng,et al.  Nonnegative Matrix Factorization for EEG Signal Classification , 2004, ISNN.

[4]  Zhaoshui He,et al.  Extended SMART Algorithms for Non-negative Matrix Factorization , 2006, ICAISC.

[5]  Andrzej Cichocki,et al.  Non-negative Matrix Factorization with Quasi-Newton Optimization , 2006, ICAISC.

[6]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[7]  Chih-Jen Lin,et al.  Projected Gradient Methods for Nonnegative Matrix Factorization , 2007, Neural Computation.

[8]  Andrzej Cichocki,et al.  Constrained non-Negative Matrix Factorization Method for EEG Analysis in Early Detection of Alzheimer Disease , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[9]  Stan Z. Li,et al.  Local non-negative matrix factorization as a visual representation , 2002, Proceedings 2nd International Conference on Development and Learning. ICDL 2002.

[10]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[11]  Andrzej Cichocki,et al.  New Algorithms for Non-Negative Matrix Factorization in Applications to Blind Source Separation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.