Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient image representation (Welling and Weber, Patt. Rec. Let., 2001). Until now, sparsity of the tensor factorization has been empirically observed in many cases, but there was no systematic way to control it. In this work, we show that a sparsity measure recently proposed for non-negative matrix factorization...

Motivated by an application in computational biology, we consider low-rank matrix factorization with {0, 1}-constraints on one of the factors and optionally convex constraints on the second one. In addition to the non-convexity shared with other matrix factorization schemes, our problem is further complicated by a combinatorial constraint set of size 2m·r, where m is the dimension of the data p...

Non-negative matrix factorization (NMF) is an unsupervised method whose aim is to find an approximate factorization Vn*m=Wn*r*Hr*m into non-negative matrices Wn*r and Hr*m. This paper presents an extension to NMF and discusses the development and the use of damped Newton based the non-negative matrix factorization called DNNMF with good convergence property for wood defects detection. We also p...

In this paper, a multi-focus image fusion method based on the weighted non-negative matrix factorization and focal point analysis is presented to improve image fusion method based on non-negative matrix factorization. The relationship among the point spread function, the optical system and imaging processing is analyzed in the method, then a weighted matrix U is built by calculating the differe...

In this paper, a procedure is reported that discuss how linear algebra can be used in image compression. The basic idea is that each image can be represented as a matrix. We apply linear algebra (QR ‎factorization and wavelet ‎transformation ‎algorithm‏s) on this matrix and get a reduced matrix out such that the image corresponding to this reduced matrix requires much less storage space than th...

using qcd factorization for the hadronic matrix elements, we show that existing data, inparticular the branching ratios br ( b →j/ψk) and br ( b →j/ψπ), can be accounted for in this approach.we analyze the decay b j /k( ) within the framework of qcd factorization. the calculation of therelevant hard-scattering kernels for twist-2 and twist-3 is completed. we calculate this decay in a special...

Clinical document contains vital information like symptom names, medication names, age, gender and some demographical information. These information can be used for giving quick relief from a disease. In existing system, they had built a system for clustering symptom names and medication names using Multi-View Non-Negative Matrix Factorization. While considering the clinical documents the facto...

In this chapter, I briefly introduce a multivariate analysis technique called non-negative matrix factorization (NMF), which has attracted a lot of attention in the field of audio signal processing in recent years. I will mention some basic properties of NMF, effects induced by the non-negative constraints, how to derive an iterative algorithm for NMF, and some attempts that have been made to a...

The multiplicative update (MU) algorithm has been used extensively to estimate the basis and coefficient matrices in nonnegative matrix factorization (NMF) problems under a wide range of divergences and regularizations. However, theoretical convergence guarantees have only been derived for a few special divergences and without regularizers. We provide a conceptually simple, self-contained, and ...

Hyperspectral remote sensing has been used in many fields, such as agriculture, military detection and mineral exploration. Hyperspectral image (HSI), despite its high spectral resolution, has lower spatial resolution than panchromatic image (PI). Therefore, it is useful yet still challenging to effectively fuse HSI and PI to obtain images with both high spectral resolution and high spatial res...