Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of manifold structure priori information data to capture excellent low-dimensional representation. However, existing methods do not consider sparse constraint, which can enhance local learning ability improve performance in practical ap...

Non-negative matrix factorization (NMF, Nature 401 (1999) 788-791) is a method to derive non-negative basis functions for sets of data that are inherently non-negative, such as color spectra. We applied NMF to Munsell color spectra and investigated the color names associated with the non-negative basis functions. NMF yields basis functions compatible with established color naming categories.

In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in general sound classification applications were measured for 300 sound recordings consisting of 6 different musical instrument classes (piano, violin, cello, flute, bassoon, and soprano saxophone). In addition, MPEG-7 basic spectral and spect...

Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow for negative-valued data and sources in the form of Semiand Convex-NMF. In this paper, we re-elaborate Semi-NMF within a full Bayesian framework. This provides solid foundat...

Reverse-convex programming (RCP) concerns global optimization of a specific class of non-convex optimization problems. We show that a recently proposed model for sparse non-negative matrix factorization (NMF) belongs to this class. Based on this result, we design two algorithms for sparse NMF that solve sequences of convex secondorder cone programs (SOCP). We work out some well-defined modifica...

In this article, we integrate the spatial-spectral information of hyperspectral image (HSI) samples into nonnegative matrix factorization (NMF) for affinity learning to address issue HSI clustering. This technique consists three main components: 1) oversegmentation computing spectral-spatial matrix; 2) NMF with guidance obtained and 3) density-based spectral clustering on final matrix. First, i...

Abstract In this paper, we propose a supervised single-channel speech enhancement method that combines Kullback-Leibler (KL) divergence-based non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM). With the integration of HMM, temporal dynamics information signals can be taken into account. This includes training stage an stage. stage, sum Poisson distribution, leading to KL ...

We report chemical routes for the synthesis of both nanocrystals and bulk crystals of methylammonium (MA) lead halide perovskites employing N-methylformamide (NMF) as a source of MA ions. Colloidal nanocrystals were prepared by a transamidation reaction between NMF and an alkyl amine (oleylamine). The nanocrystals showed photoluminescence quantum yields reaching 74% for MAPbBr3 and 60% for MAPb...

We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not. The theorems are illustrated by several examples showing the use of the theorems and their limitations. We have shown that corruption of a unique NMF matrix by additive noise leads to a noisy estimation of...