Abstract

Nonnegative matrix factorization (NMF) has become a prominent technique for the analysis of image databases, text databases, and other information retrieval and clustering applications. The problem is most naturally posed as continuous optimization. In this report, we define an exact version of NMF. Then we establish several results about exact NMF: (i) that it is equivalent to a problem in polyhedral combinatorics; (ii) that it is NP-hard; and (iii) that a polynomial-time local search heuristic exists.

MSC codes

  1. 15A23
  2. 15A48
  3. 68T05
  4. 90C60
  5. 90C26

Keywords

  1. nonnegative matrix factorization
  2. nonnegative rank
  3. complexity
  4. NP-hard
  5. data mining
  6. feature detection

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References

1.
N. Asgarian and R. Greiner, Using Rank-1 Biclusters to Classify Microarray Data, Department of Computing Science and the Alberta Ingenuity Center for Machine Learning, University of Alberta, Edmonton, AB, Canada, 2006.
2.
S. Bergmann, J. Ihmels, and N. Barkai, Iterative signature algorithm for the analysis of large-scale gene expression data, Phys. Rev. E (3), 67 (2003), pp. 031902-1–031902-18.
3.
M. Biggs, A. Ghodsi, and S. Vavasis, Nonnegative matrix factorization via rank-one downdating, in International Conference on Machine Learning, 2008, available online from http://icm12008.cs.helsinki.fi/papers/667/pdf.
4.
L. Blum, F. Cucker, M. Shub, and S. Smale, Complexity of Real Computation, Springer-Verlag, New York, 1998.
5.
C. Boutsidis and E. Gallopoulos, SVD based initialization: A head start for nonnegative matrix factorization, Pattern Recognition, 41 (2008), pp. 1350–1362.
6.
J. Cohen and U. Rothblum, Nonnegative ranks, decompositions and factorizations of nonnegative matrices, Linear Algebra Appl., 190 (1993), pp. 149–168.
7.
J. Edmonds, Systems of distinct representatives and linear algebra, J. Res. Nat. Bureau of Standards, 71B (1967), pp. 241–245.
8.
M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman, San Francisco, 1979.
9.
N. Gillis, Approximation et sous-approximation de matrices par factorisation positive: Algorithmes, complexité et applications, Master's Thesis, Université Catholique de Louvain, Louvain-la-Neuve, Belgium, 2006 (in French).
10.
G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd ed., Johns Hopkins University Press, Baltimore, 1996.
11.
T. Hofmann, Probabilistic latent semantic analysis, in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, 1999, ACM Press, pp. 50–57.
12.
H. Kim and H. Park, Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis, Bioinformatics, 23 (2007), pp. 1495–1502.
13.
D. Lee and H. Seung, Learning the parts of objects by non-negative matrix factorization, Nature, 401 (1999), pp. 788–791.
14.
L. B. Thomas, Rank factorization of nonnegative matrices (A. Berman), SIAM Rev., 16 (1974), pp. 393–394.

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Published In

cover image SIAM Journal on Optimization
SIAM Journal on Optimization
Pages: 1364 - 1377
ISSN (online): 1095-7189

History

Submitted: 3 December 2007
Accepted: 8 June 2009
Published online: 16 October 2009

MSC codes

  1. 15A23
  2. 15A48
  3. 68T05
  4. 90C60
  5. 90C26

Keywords

  1. nonnegative matrix factorization
  2. nonnegative rank
  3. complexity
  4. NP-hard
  5. data mining
  6. feature detection

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