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Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

15. FWA Application on Non-negative Matrix Factorization

verfasst von : Ying Tan

Erschienen in: Fireworks Algorithm

Verlag: Springer Berlin Heidelberg

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Abstract

This chapter presents the use of swarm intelligence algorithms for non-negative matrix factorization (NMF) Janecek and Tan (2011) International Journal of Swarm Intelligence Research (IJSIR) 2(4):12–34, [1]. The NMF is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. Here, we present our efforts to improve the convergence and approximation quality of NMF using five different meta-heuristics based on swarm intelligence. Several properties of the NMF objective function motivate the utilization of meta-heuristics: this function is non-convex, discontinuous, and may possess many local minima. The proposed optimization strategies are twofold: On one hand, we present a new initialization strategy for NMF in order to initialize the NMF factors prior to the factorization; on the other hand, we present an iterative update strategy which improves the accuracy per runtime for the multiplicative update NMF algorithm.

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Vorheriges Kapitel Implementation of Fireworks Algorithm Based on GPU
Nächstes Kapitel FWA Applications on Clustering, Pattern Recognition, and Inversion Problem
Literatur
1.
A. Janecek, Y. Tan, Swarm intelligence for non-negative matrix factorization. Int. J. Swarm Intell. Res. (IJSIR) 2(4), 12–34 (2011)
2.
D.D. Lee, H.S. Seung, Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRef
3.
M.W. Berry, M. Browne, A.N. Langville, V. Paul Pauca, R.J. Plemmons, Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal. 52(1), 155–173 (2007)MATHCrossRef
4.
A.N. Langville, C.D. Meyer, R. Albright, J. Cox, D. Duling, Utilizing nonnegative matrix factorization for e-mail classification problems. Survey of Text Mining III: Application and Theory (Wiley, New York 2010), pp. 57–80
5.
K. Stadlthanner, D. Lutter, F.J. Theis, E.W. Lang, A.M. Tom, P. Georgieva, et al., Sparse nonnegative matrix factorization with genetic algorithms for microarray analysis, in International Joint Conference on Neural Networks, IJCNN 2007 (IEEE, 2007), pp. 294–299
6.
T. Blackwell, Particle swarm optimization in dynamic environments, in Evolutionary Computation in Dynamic and Uncertain Environments (Springer, Berlin, 2007), pp. 29–49
7.
R. Chiong, Nature-Inspired Algorithms for Optimisation, vol. 193 (Springer, Berlin, 2009)
8.
R.C. Eberhart, Y. Shi, J. Kennedy, Swarm Intelligence (Elsevier, Indianapolis, 2001)
9.
P. Paatero, U. Tapper, Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111–126 (1994)CrossRef
10.
11.
H. Kim, H. Park, Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713–730 (2008)MATHMathSciNetCrossRef
12.
M.N. Schmidt, H. Laurberg, Nonnegative matrix factorization with Gaussian process priors. Comput. Intell. Neurosci. 2008, 3 (2008)
13.
A. Janecek, S. Schulze Grotthoff, W.N Gansterer, LibNMF—a library for nonnegative matrix factorization. Comput. Inf. 30(2), 205–224 (2011)
14.
S. Wild, J. Curry, A. Dougherty, Improving non-negative matrix factorizations through structured initialization. Pattern Recognit. 37(11), 2217–2232 (2004)CrossRef
15.
Y. Xue, C.S. Tong, Y. Chen, W.-S. Chen, Clustering-based initialization for non-negative matrix factorization. Appl. Math. Comput. 205(2), 525–536 (2008)MATHMathSciNetCrossRef
16.
C. Boutsidis, E. Gallopoulos, SVD based initialization: a head start for nonnegative matrix factorization. Pattern Recognit. 41(4), 1350–1362 (2008)MATHCrossRef
17.
V. Snel, J. Plato, P. Krmer, Developing genetic algorithms for boolean matrix factorization. Databases Texts 61, (2008)
18.
A. Janecek, Y. Tan, Using population based algorithms for initializing nonnegative matrix factorization. Advances in Swarm Intelligence (Springer, Berlin, 2011), pp. 307–316
19.
A. Janecek, Y. Tan, Iterative improvement of the multiplicative update nmf algorithm using nature-inspired optimization, in 2011 Seventh International Conference on Natural Computation (ICNC), vol. 3 (IEEE, 2011), pp. 1668–1672
20.
M.W. Berry, Large-scale sparse singular value computations. Int. J. Supercomput. Appl. 6(1), 13–49 (1992)
21.
I. Jolliffe, Principal Component Analysis (Wiley Online Library, 2005)
22.
P.N. Tan, M. Steinbach, V. Kumar. Introduction to Data Mining. Pearson Education, Inc., London (2006)
23.
M.W. Berry, Z. Drmac, E.R. Jessup, Matrices, vector spaces, and information retrieval. SIAM Rev. 41(2), 335–362 (1999)
24.
Q. Zhang, M.W. Berry, B.T. Lamb, T. Samuel, A parallel nonnegative tensor factorization algorithm for mining global climate data, in Computational Science–ICCS 2009 (Springer, Berlin, 2009), pp. 405–415
25.
A.N. Langville, C.D. Meyer, R. Albright, J. Cox, D. Duling, Initializations for the nonnegative matrix factorization, in Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Citeseer, 2006), pp. 23–26
26.
D. Bratton, J. Kennedy, Defining a standard for particle swarm optimization, in Swarm Intelligence Symposium, SIS 2007 (IEEE, 2007), pp. 120–127
27.
C.J.A. Bastos Filho, F.B. de Lima Neto, A.J.C.C. Lins, A.I.S. Nascimento, M.P. Lima, Fish school search, in Nature-Inspired Algorithms for Optimisation (Springer, Berlin, 2009), pp. 261–277
28.
Y. Tan, Y. Zhu, Fireworks algorithm for optimization, in Advances in Swarm Intelligence (Springer, Berlin, 2010), pp. 355–364
29.
R.L. Haupt, S.E. Haupt, Practical Genetic Algorithms (Wiley, New York, 2004)
30.
K. Price, R.M. Storn, J.A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Springer, Berlin, 2006)
31.
M.E.H. Pedersen, SwarmOps: Black-Box Optimization in ANSI C (Hvass Laboratories, Southampton, 2008)
Metadaten
Titel
FWA Application on Non-negative Matrix Factorization
verfasst von
Ying Tan
Copyright-Jahr
2015
Verlag
Springer Berlin Heidelberg
Buch
Fireworks Algorithm

Print ISBN: 978-3-662-46352-9

Electronic ISBN: 978-3-662-46353-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-662-46353-6_15