Semi-NMF is a matrix factorization technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original features contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not in...

NMF (Non-negative Matrix Factorization) has been one of the most useful techniques for audio signal analysis in recent years. In particular, supervised NMF, in which a large number of samples is used for analyzing a signal, is garnering much attention in sound source separation or noise reduction research. However, because such methods require all the possible samples for the analysis, it is ha...

The most popular algorithms for Nonnegative Matrix Factorization (NMF) belong to a class of multiplicative Lee-Seung algorithms which have usually relative low complexity but are characterized by slow-convergence and the risk of getting stuck to in local minima. In this paper, we present and compare the performance of additive algorithms based on three different variations of a projected gradie...

Polar solvents, which induce differentiation in murine and human tumor cells, enhance the effect of ionizing radiation on cultured mouse mammary and human colon cancer cells. To determine whether this enhancement occurs in vivo, DLD-2 human colon carcinoma xenografts in nude mice were treated with combinations of 6 MV photon irradiation, the polar solvent N-methylformamide (NMF), or combination...

Nonnegative matrix factorization (NMF) provides a lower rank approximation of a nonnegative matrix, and has been successfully used as a clustering method. In this paper, we offer some conceptual understanding for the capabilities and shortcomings of NMF as a clustering method. Then, we propose Symmetric NMF (SymNMF) as a general framework for graph clustering, which inherits the advantages of N...

In this paper, a non-negative matrix factorization (NMF)based document clustering approach is proposed for the cluster-based language model for spoken document retrieval. The retrieval language model comprises three different unigram models: a whole corpus collect-based unigram, documentbased unigram, and a document clustering-based unigram. They are combined with double linear interpolations. ...

Nonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor Factorization (NTF) have become prominent techniques for blind sources separation (BSS), analysis of image databases, data mining and other information retrieval and clustering applications. In this paper we propose a family of efficient algorithms for NMF/NTF, as well as sparse nonnegative coding and represent...

Non-negative matrix factorization (NMF) is an increasingly popular feature extraction method. Since it is designed to fit training samples using linear combination of non-negative basis vectors, it is particular suitable for image applications as it affords intuitive localized interpretations. However, in this space defined by the NMF basis images, there has not been any systematic research to ...

Nonnegative Matrix Factorization (NMF) with Kullback-Leibler Divergence (NMF-KL) is one of the most significant NMF problems and equivalent to Probabilistic Latent Semantic Indexing (PLSI), which has been successfully applied in many applications. For sparse count data, a Poisson distribution and KL divergence provide sparse models and sparse representation, which describe the random variation ...

The changes in endopolygalacturonase (endo-PG) levels and endo-PG expression in nonmelting flesh (NMF) and melting flesh (MF) peach fruits (Prunus persica) during softening were studied. The endo-PG gene was analysed to identify polymorphisms exploitable for early marker-assisted selection (MAS) of flesh texture. The role of endo-PG in softening was assessed by western and northern blotting and...