Searching and mining biomedical literature database, such as MEDLINE, is the main source of generating scientific hypothesis for biomedical researchers. Through grouping similar documents together, clustering techniques can facilitate user’s need of effectively finding interested documents. Since non-negative matrix factorization (NMF) can effectively capture the latent semantic space with non-...

Introduction to (near-separable) NMF • NMF Problem: X ∈ Rm×n + is a matrix with nonnegative entries, and we want to compute a nonnegative matrix factorization (NMF) X = WH, where W ∈ Rm×r + and H ∈ Rr×n + . When r < m, this problem is NP-hard. • A separable matrix is one that admits a nonnegative factorization where W = X(:,K), i.e. W is just consists of some subset of the columns of X . A near...

Nonnegative matrix factorization NMF is a popular tool for analyzing the latent structure of nonnegative data. For a positive pairwise similarity matrix, symmetric NMF SNMF and weighted NMF WNMF can be used to cluster the data. However, both of them are not very efficient for the ill-structured pairwise similarity matrix. In this paper, a novel model, called relationship matrix nonnegative deco...

Discovering a representation that reflects the structure of a dataset is a first step for many inference and learning methods. This paper aims at finding a hierarchy of localized speech features that can be interpreted as parts. Non-negative matrix factorization (NMF) has been proposed recently for the discovery of parts-based localized additive representations. Here, I propose a variant of thi...

Established in 1990, the New Milford Farms (NMF) composting facility serves as a low-cost means of disposal for various industrial residues from Nestle USA, a large international food company. The NMF facility takes in organic feedstocks ranging from spent coffee grounds and industrial wastewater sludge to leaves and brush from nearby residents, and combines them to create fertilizing compost u...

Detecting and tracking latent factors from temporal data is an important task. Most existing algorithms for latent topic detection such as Nonnegative Matrix Factorization (NMF) have been designed for static data. These algorithms are unable to capture the dynamic nature of temporally changing data streams. In this paper, we put forward an online NMF (ONMF) algorithm to detect latent factors an...

In this paper, we extend the original non-negative matrix factorization (NMF) to kernel NMF (KNMF). The advantages of KNMF over NMF are: 1) it could extract more useful features hidden in the original data through some kernel-induced nonlinear mappings; 2) it can deal with data where only relationships (similarities or dissimilarities) between objects are known; 3) it can process data with nega...

The solvation of N-methylformamide (NMF) by dimethylsulfoxide (DMSO) in a 20% NMF/DMSO liquid mixture is investigated using a combination of neutron diffraction augmented with isotopic substitution and Monte Carlo simulations. The aim is to investigate the solute-solvent interactions and the structure of the solution. The results point to the formation of a hydrogen bond (H-bond) between the H ...

Nonnegative matrix factorization method is a kind of new matrix rotting method. It is an effective tool for large data processing and analysis. At the same time, NMF has an important performance on in intellectual information processing and pattern recognition. We then aim for increasing an efficiency and accuracy of data clustering and classification based on NMF. NMF method is used to reduce ...

We propose a method based on Cholesky decomposition for Non-negative Matrix Factorization (NMF). NMF enables to learn local representation due to its non-negative constraint. However, when utilizing NMF as a representation leaning method, the issues due to the non-orthogonality of the learned representation has not been dealt with. Since NMF learns both feature vectors and data vectors in the f...