Application of Data Classification Method Based on Non-Negative Matrix Factorization

Application of Non-negative Matrix Factorization to Musical Instrument Classification

In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in general sound classification applications were measured for 300 sound recordings consisting of 6 different musical instrument classes (piano, violin, cello, flute, bassoon, and soprano saxophone). In addition, MPEG-7 basic spectral and spect...

Application of non-negative matrix factorization to LC/MS data

Liquid Chromatography-Mass Spectrometry (LC/MS) provides large datasets from which one needs to extract the relevant information. Since these data are made of non-negative mixtures of non-negative mass spectra, nonnegative matrix factorization (NMF) is well suited for its processing, but it has barely been used in LC/MS. Also, these data are very difficult to deal with since they are usually co...

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...

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