Biological network clustering by robust NMF Conference Paper uri icon

abstract

  • Copyright © 2014 ACM. We propose a Robust Non-negative Matrix Factorization (RNMF) formulation by introducing L1-norm regularization terms for decomposed factors to cluster noisy biological net- works for identification of functional modules. To solve ro- bust NMF, we develop an accelerated alternative proximal method, which takes advantages of a fast iterative shrinkage- Thresholding strategy to update each factorized component at each step. We compare the performance of this acceler- Ated proximal method with a multiplicative algorithm and a general proximal method for the same RNMF formulation. Experiments on synthetic networks and Protein-Protein In- Teraction (PPI) networks demonstrate that the accelerated proximal method is superior to the other algorithms in terms of efficiency and effectiveness for functional module identification.

author list (cited authors)

  • Wang, Y., & Qian, X.

citation count

  • 2

publication date

  • September 2014