Dimension group of a non-negative integer irreducible matrix

An always convergent method for finding the spectral radius of an irreducible non-negative matrix

An always convergent method is used to calculate the spectral radius of an irreducible non-negative matrix. The method is an adaptation of a method of Collatz (1942), and has similarities to both the power method and the inverse power method. For large matrices it is faster than the eig routine in Matlab. Special attention is paid to the step-by-step improvement of the bounds and the subsequent...

Group Sparse Non-negative Matrix Factorization for Multi-Manifold Learning

Many observable data sets such as images, videos and speech can be modeled by a mixture of manifolds which are the result of multiple factors (latent variables). In this paper, we propose a novel algorithm to learn multiple linear manifolds for face recognition, called Group Sparse Non-negative Matrix Factorization (GSNMF). Via the group sparsity constraint imposed on the column vectors of the ...

Non-negative matrix factorization

Non-Negative Matrix Factorisation of Compressively Sampled Non-Negative Signals

The new emerging theory of Compressive Sampling has demonstrated that by exploiting the structure of a signal, it is possible to sample a signal below the Nyquist rate and achieve perfect reconstruction. In this short note, we employ Non-negative Matrix Factorisation in the context of Compressive Sampling and propose two NMF algorithms for signal recovery—one of which utilises Iteratively Rewei...

A new approach for building recommender system using non negative matrix factorization method

Nonnegative Matrix Factorization is a new approach to reduce data dimensions. In this method, by applying the nonnegativity of the matrix data, the matrix is ​​decomposed into components that are more interrelated and divide the data into sections where the data in these sections have a specific relationship. In this paper, we use the nonnegative matrix factorization to decompose the user ratin...