Non-negative Matrix Factorization: Robust Extraction of Extended Structures

Non-negative Matrix Factorization: Robust Extraction of Extended Structures

We apply the vectorized Non-negative Matrix Factorization (NMF) method to post-processing of direct imaging data for exoplanetary systems such as circumstellar disks. NMF is an iterative approach, which first creates a non-orthogonal and non-negative basis of components using given reference images, then models a target with the components. The constructed model is then rescaled with a factor t...

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...

Non-negative matrix factorization

Inferential, robust non-negative matrix factorization analysis of microarray data

MOTIVATION Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory; even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Correlations among predictors are used to improve the statistical analysis....

The Feature Extraction and Recognition of Phone Image Based on Robust Sparse Non-Negative Matrix Factorization

Sparse non-negative matrix factorization algorithm can project image data effectively. It plays an important role in image matching and recognition. In order to improve the effectiveness of SNMF algorithm, which is used in feature extraction of image data with noises, we added a noise term and combined it with SNMF algorithm. Then, we proposed a new sparse optimization objective function and wo...