When does non-negative matrix factorization give a correct decomposition into parts?

Advances in Neural Information Processing Systems

Volumn , Issue , 2004, Pages

  Donoho, David ^a   Stodden, Victoria ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS;

DATA POINTS; GENERATIVE MODEL; NONNEGATIVE MATRIX FACTORIZATION; ORTHANT; SIMPLICIAL CONES; SYNTHETIC IMAGES;

FACTORIZATION;

EID: 49149085985     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. M. Craig. Minimum-volume transforms for remotely sensed data. IEEE Transactions on Geoscience and Remote Sensing, 32(3):542-552, May 1994.
2. M. Juvela, K. Lehtinen, and P. Paatero. The use of positive matrix factorization in the analysis of molecular line spectra from the thumbprint nebula. In D. P. Clemens and R. Barvainis, editors, Clouds, Cores, and Low Mass Stars, volume 65 of ASP Conference Series, 176-180, 1994.
3. M. Juvela, K. Lehtinen, and P. Paatero. The use of positive matrix factorization in the analysis of molecular line spectra. MNRAS, 280:616-626, 1996.
4. D. Lee and S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788-791, 1999.
5. M. Plumbley. Conditions for nonnegative independent components analysis. Signal Processing Letters, IEEE, 9(6):177-180, 2002.
6. A. Polissar, P. Hopke, W. Malm, and J. Sisler. Atmospheric aerosol over alaska 1. spatial and seasonal variability. Journal of Geophysical Research, 103(D15):19035-19044, August 1998.
7. A. Polissar, P. Hopke, W. Malm, and J. Sisler. Atmospheric aerosol over alaska 2. elemental composition and sources. Journal of Geophysical Research, 103(D15):19045-19057, August 1998.
8. R. T. Rockefellar. Convex Analysis, Princeton University Press, 1970.
9. W. Size. Use and Abuse of Statistical Methods in the Earth Sciences, chapter 3, pages 33-46. Oxford University Press, 1987.