Scalable non-negative matrix tri-factorization

Scalable non-negative matrix tri-factorization: Supplementary material

We provide further details on performance analysis for our block-wise matrix tri-factorization. In particular, we include analysis of orthogonal matrix tri-factorization that is discussed in our manuscript but whose results, due to conceptual similarity with non-orthogonal factorization were not included in there. We also present the impact of communication overhead on both non-orthogonal and o...

Non-negative matrix factorization

Non-negative Matrix Tri-Factorization for co-clustering: An analysis of the block matrix

Article history: Received 22 April 2014 Received in revised form 7 December 2014 Accepted 31 December 2014 Available online 9 January 2015

Bayesian Non-negative Matrix Factorization

We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates. We discuss how the Gibbs sampler can be used for model ...

Robust non-negative matrix factorization

Non-negative matrix factorization (NMF) is a recently popularized technique for learning partsbased, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) i...