Statistical optimization of Non-Negative Matrix Factorization

Journal of Machine Learning Research

Volumn 15, Issue , 2011, Pages 128-136

  Korattikara, Anoop ^a   Boyles, Levi ^a   Welling, Max ^a   Kim, Jingu ^b   Park, Haesun ^b  

^a Irvine   (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC NORMALITY; COORDINATE DESCENT; DESIGN MATRIX; DIMENSIONALITY REDUCTION METHOD; FREQUENTIST; HYPOTHESIS TESTS; LEAST SQUARES PROBLEMS; LEAST-SQUARES ESTIMATOR; NONNEGATIVE MATRIX FACTORIZATION; PIVOTING METHODS; REAL-WORLD DATASETS; STATISTICAL OPTIMIZATION; STOPPING CRITERIA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; OPTIMIZATION; PARKS; RANDOM PROCESSES;

LEAST SQUARES APPROXIMATIONS;

EID: 84862279730     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (18)

1. Bertsekas, D. P. (1999). Nonlinear programming. Belmont, Mass: Athena Scientific.
2. Bottou, L., & Bousquet, O. (2008). Learning using large datasets. Mining Massive DataSets for Security, NATO ASI Workshop Series.
3. Boyles, L. (2010). Statistical tests for optimization efficiency. Master's thesis, University of California, Irvine.
4. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 886-893).
5. Kim, D., Sra, S., & Dhillon, I. (2007). Fast newton-type methods for the least squares nonnegative matrix approximation problem. Proceedings of the 2007 SIAM International Conference on Data Mining.
6. Kim, H., & Park, H. (2008a). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30, 713-730.
7. Kim, J., & Park, H. (2008b). Toward faster nonnegative matrix factorization: A new algorithm and comparisons. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (ICDM) (pp. 353-362).
8. Kushner, H., & Yin, G. (2003). Stochastic approximation and recursive algorithms and applications. Springer Verlag.
9. Lawson, C. L., & Hanson, R. J. (1995). Solving least squares problems. Society for Industrial and Applied Mathematics.
10. LeCun, Y., Bottou, L., Orr, G., & Muller, K. (1998). Efficient backprop. Neural networks: Tricks of the trade, 546-546.
11. Lin, C.-J. (2007). Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19, 2756-2779.
12. Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. The Journal of Machine Learning Research, 11, 19-60.
13. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika (pp. 519-530).
14. Orr, G. B. (1996). Removing noise in on-line search using adaptive batch sizes. NIPS (pp. 232-238).
15. Robbins, H., & Monro, S. (1951). A stochastic approximation method. The Annals of Mathematical Statistics, 22, 400-407.
16. Spall, J. (2003). Introduction to stochastic search and optimization: estimation, simulation, and control. John Wiley and Sons.
17. Verbeek, M. (2000). A guide to modern econometrics. John Wiley and Sons.
18. Yu., B. (2007). Embracing statistical challenges in the information technology age. Technometrics, American Statistical Association and the American Society for Quality, 49, 237-248.