In this paper, a 2kb/s Waveform Interpolation speech coder is proposed based on non-negative matrix factorization (NMF). In characteristic waveforms (CWs) decomposition, band-partitioning initialization constraints were set to basis vectors before NMF was carried out. This decomposition method only requires speech signal from the current frame, and can yield high decomposition quality with low ...

Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden dat...

In the last decade, non-negative matrix factorization (NMF) has become a widely used method for solving problems in data mining and pattern recognition. The NMF in its present state can be traced back to the work of Paatero and Tapper in 1994 at the University of Helsinki under the name, “positive matrix factorization” [1]. This technique was popularized by Lee and Seung in 1999 under its curre...

This paper proposes a discriminative learning method for Nonnegative Matrix Factorization (NMF)-based Voice Conversion (VC). NMF-based VC has been researched because of the natural-sounding voice it produces compared with conventional Gaussian Mixture Model (GMM)-based VC. In conventional NMF-based VC, parallel exemplars are used as the dictionary; therefore, dictionary learning is not adopted....

nonnegative matrix factorization (nmf) is a common method in data mining that have been used in different applications as a dimension reduction, classification or clustering method. methods in alternating least square (als) approach usually used to solve this non-convex minimization problem.  at each step of als algorithms two convex least square problems should be solved, which causes high com...

The standard non-negative matrix factorization (NMF) is a popular method to obtain low-rank approximation of a non-negative matrix, which is also powerful for clustering and classification in machine learning. In NMF each data sample is represented by a vector of features of the same dimension. In practice, we often have good side information for a subset of data samples. These side information...

In this paper, we use non-negative matrix factorization (NMF) to refine the document clustering results. NMF is a dimensional reduction method and effective for document clustering, because a term-document matrix is high-dimensional and sparse. The initial matrix of the NMF algorithm is regarded as a clustering result, therefore we can use NMF as a refinement method. First we perform min-max cu...

SUMMARY Non-negative matrix factorization (NMF) is an increasingly used algorithm for the analysis of complex high-dimensional data. BRB-ArrayTools is a widely used software system for the analysis of gene expression data with almost 9000 registered users in over 65 countries. We have developed a NMF analysis plug-in in BRB-ArrayTools for unsupervised sample clustering of microarray gene expres...

A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF with explicit orthogonality and derive general kernelbased orthogonal multiplicative update rules to solve the subspace clustering problem. In nonlin...