The amount of tissue available in the breast, commonly characterized by the breast density, is highly correlated with breast cancer. In fact, dense breasts have higher risk of developing breast cancer. On the other hand, breast density influences the mammographic interpretation since it decreases the sensitivity of breast cancer detection. This sensitivity decrease is due to the fact that both ...

We describe Nonnegative Double Singular Value Decomposition (NNDSVD), a new method designed to enhance the initialization stage of nonnegative matrix factorization (NMF). NNDSVD can readily be combined with existing NMF algorithms. The basic algorithm contains no randomization and is based on two SVD processes, one approximating the data matrix, the other approximating positive sections of the ...

propose a novel approach, which is tive matrix factorization (NMF), to ide expression data. One of NMF it can directly process these data tion. Firstly, we design an optimal F approach. Compared with the rithms, our algorithm is more stable ry fast. We have coded the final hly optimized C. Secondly; we of NMF in the extraction of the erns from genome wide expression e simulation experiments are...

This article presents theoretical, algorithmic and experimental results about nonnegative matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is underlain by a well-defined statistical model of superimposed Gaussian components and is equivalent to maximum likelihood estimation of variance parameters. This setting can accommodate regularization constraints o...

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...

In this paper, I will give a brief introduction to a data 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 mad...

We generalize the convolutional NMF by taking the β-divergence as the loss function, add a regularizer for sparsity in the form of an elastic net, and provide multiplicative update rules for its factors in closed form. The new update rules embed the β-NMF, the standard convolutional NMF, and sparse coding alias basis pursuit. We demonstrate that the originally published update rules for the con...

Nonnegative Matrix Factorization (NMF) has become an increasingly popular method in the field of non-stationary speech denoising. However most NMF-based algorithms assume prior knowledge about the background noise, which is often not available in time-varying and mobile environments. In this paper, we propose a two-step NMF based speech-noise separation algorithm to address this issue. This alg...