By Topic

Large Scale Graph Regularized Non-Negative Matrix Factorization With {cal \ell }_1 Normalization Based on Kullback–Leibler Divergence

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Meng Sun ; Dept. of Electr. Eng., Katholieke Univ. Leuven, Leuven, Belgium ; Van Hamme, H.

We propose a novel algorithm for graph regularized non-negative matrix factorization (NMF) with ℓ1 normalization based on the Kullback-Leibler divergence. The ℓ1 normalization is imposed to overcome the scaling ambiguity in earlier work on graph regularized NMF (GNMF) in [D. Cai, X. He, J. Han, and T. Huang, “Graph regularized non-negative matrix factorization for data representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, pp. 1548-1560, 2011]. The algorithm only involves element-wise iterative updating to ensure both non-negativity of the solution and convergence. Its element-wise structure makes the proposed algorithm suitable for large scale problems. Experiments on spoken pattern discovery on the TIDIGITS database and on image clustering of the PIE dataset show that the algorithm outperforms the previous one with a better accuracy and a lower computational complexity.

Published in:

Signal Processing, IEEE Transactions on  (Volume:60 ,  Issue: 7 )