Sparse General Non-Negative Matrix Factorization Based on Left Semi-Tensor Product

Sparse non-negative tensor factorization using columnwise coordinate descent

Many applications in computer vision, biomedical informatics, and graphics deal with data in the matrix or tensor form. Non-negative matrix and tensor factorization, which extract data-dependent non-negative basis functions, have been commonly applied for the analysis of such data for data compression, visualization, and detection of hidden information (factors). In this paper, we present a fas...

Approximate L0 constrained non-negative matrix and tensor factorization

Non-negative matrix factorization (NMF), i.e. V ≈ WH where both V, W and H are non-negative has become a widely used blind source separation technique due to its part based representation. The NMF decomposition is not in general unique and a part based representation not guaranteed. However, imposing sparseness both improves the uniqueness of the decomposition and favors part based representati...

Non-stationary Noise Estimation Based on Non-negative Matrix Factorization

In this paper, we apply a non-negative matrix factorization (NMF) technique to propose a method of estimating noise occurring in non-stationary environments. In the proposed method, the basis matrix of the target noise is first obtained via NMF training. The noise basis is then applied to estimate an activation matrix of the target noise from the noisy signal. The proposed method is finally app...

Non-negative matrix factorization

Separation of Reflection Components by Sparse Non-negative Matrix Factorization

This paper presents a novel method for separating reflection components in a single image based on the dichromatic reflection model. Our method is based on a modified version of sparse non-negative matrix factorization (NMF). It simultaneously performs the estimation of body colors and the separation of reflection components through optimization. Our method does not use a spatial prior such as ...