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...

Non-negative Matrix Factorization (NMF) is a traditional unsupervised machine learning technique for decomposing a matrix into a set of bases and coefficients under the non-negative constraint. NMF with sparse constraints is also known for extracting reasonable components from noisy data. However, NMF tends to give undesired results in the case of highly sparse data, because the information inc...

Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori such as labels or important features, methods have been developed to perform classification tasks; however, most can both do not allow for guidance of the topics features. In this paper, we propose novel method, namely Guided Semi-Supervised ...

Abstract—What matrix factorization methods do is reduce the dimensionality of data without losing any important information. In this work, we present Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other factorization. We discuss main optimization algorithms, used to solve NMF problem, and their convergence. The paper also contains a comparative study betwe...

Non-negative matrix factorization is used to find a basic and weight approximate the non-negative matrix. It has proven be powerful low-rank decomposition technique for multivariate data. However, its performance largely depends on assumption of fixed number features. This work proposes new probabilistic which factorizes into factor with 0,1 constraints In order automatically learn potential bi...