Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Unfortunately, the L1 re...

Nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used for numerous applications including text mining, computer vision, pattern discovery, and bioinformatics. A mathematical formulation for NMF appears as a non-convex optimization problem, and various types of algorithms have been devised to solve the problem. The alternating nonnegative least squares ...

This paper presents an improvement of the classical Non-negative Matrix Factorization (NMF) approach, for dealing with local representations of image objects. NMF, when applied to global data representations such as faces presents a high ability to represent local features of the original data in an unsupervised way. However, when applied to local representations NMF generates redundant basis. ...

The outermost layer of the skin, the stratum corneum (SC), is a lipid-protein membrane that experiences considerable osmotic stress from a dry and cold climate. The natural moisturizing factor (NMF) comprises small and polar substances, which like osmolytes can protect living systems from osmotic stress. NMF is commonly claimed to increase the water content in the SC and thereby protect the ski...

Given a vector space model encoding of a large data set, a usual starting point for data analysis is rank reduction [1]. However, standard rank reduction techniques such as the QR, Singular Value (SVD), and Semi-Discrete (SDD) decompositions and Principal Component Analysis (PCA) produce low rank bases which do not respect the non-negativity or structure of the original data. Non-negative Matri...

Recently, Non-negative Matrix Factorisation (NMF) has found application in separation of individual sound sources. NMF decomposes the spectrogram of an audio mixture into an additive parts based representation where the parts typically correspond to individual notes or chords. However, there is a need to cluster the NMF basis functions to their sources. Although, many attempts have been made to...

In this paper, we propose a novel approach to noise suppression using multiple distributed recording devices with stereo microphones. the proposed method, based on phase information is applied synchronous signals captured by each device and then output are utilized for transfer-function-gain nonnegative matrix factorization (NMF) as extra input signals. We intended estimate target signal more a...

Principal Component Analysis (PCA) is a classical method which is commonly used for human face images representation in face super-resolution. But the features extracted by PCA are holistic and difficult to have semantic interpretation. In order to synthesize a high-resolution face image with structural details, we propose a face super-resolution algorithm based on non-negative matrix factoriza...

Building a diversified portfolio is an appealing strategy in the analysis of stock market dynamics. It aims at reducing risk in market capital investments. Grouping stocks by similar latent trend can be cast into a clustering problem. The classical K-Means clustering algorithm does not fit the task of financial data analysis. Hence, we investigate Non-negative Matrix Factorization (NMF) techniq...

Non-negative matrix factorization (NMF), proposed recently by Lee and Seung, has been applied to many areas such as dimensionality reduction, image classification image compression, and so on. Based on traditional NMF, researchers have put forward several new algorithms to improve its performance. However, particular emphasis has to be placed on the initialization of NMF because of its local co...