The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence, approximation quality, and classification accuracy of NMF using five different meta-heuristics based on swarm intelligence. Several properties of the NMF objective function...

Non-negative Matrix Factorization (NMF) has already been applied to learn speaker characterizations from single or nonsimultaneous speech for speaker recognition applications. It is also known for its good performance in (blind) source separation for simultaneous speech. This paper explains how NMF can be used to jointly solve the two problems in a multichannel speaker recognizer for simultaneo...

There are two problems need to be dealt with for Non-negative Matrix Factorization (NMF): choose a suitable rank of the factorization and provide a good initialization method for NMF algorithms. This paper aims to solve these two problems using Singular Value Decomposition (SVD). At first we extract the number of main components as the rank, actually this method is inspired from [1, 2]. Second,...

The objective function of CICF, with the non-negative constraints on V and E, is a special case of Non-negative Matrix Factorization (NMF) (Lee & Seung, 1999; Recht et al., 2012; Seung & Lee, 2001; Yang et al., 2012), and can be solved by a multiplicative update approach (Seung & Lee, 2001; Yang & Oja, 2010). However, this approach suffers from the fluctuation problem in convergence (Yang & Oja...

We proposed automatic factorization method of biological signals measured by Fluorescence Correlation Spectroscopy (FCS). Since the signals are composed from several positive components, the signals are decomposed by using the idea of Non-negative matrix factorization (NMF). Each component is represented by model functions and the signals are factorized as the non-negative sum of the model func...

In this paper, we propose a new ensemble document clustering method. The novelty of our method is the use of Non-negative Matrix Factorization (NMF) in the generation phase and a weighted hypergraph in the integration phase. In our experiment, we compared our method with some clustering methods. Our method achieved the best results.

The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a regionor partbased representation of objects and images. The positive space defined with NMF lacks of a suitable metric and this paper experimentally compares NMF to Principal Component Analysis (PCA) in the context of classification trying to determine the ...

In image compression and feature extraction, linear expansions are standardly used. It was recently pointed out by Lee and Seung that the positivity or non-negativity of a linear expansion is a very powerful constraint, that seems to lead to sparse representations for the images. Their technique, called Non-negative Matrix Factorization (NMF), was shown to be a useful technique in approximating...

We propose a simple and efficient approach to learning sparse models. Our approach consists of (1) projecting the data into a lower dimensional space, (2) learning a dense model in the lower dimensional space, and then (3) recovering the sparse model in the original space via compressive sensing. We apply this approach to Non-negative Matrix Factorization (NMF), tensor decomposition and linear ...