This chapter introduces the basic formulations of nonnegative matrix factorization (NMF) and
outlines the theoretical foundations on NMF for clustering and presents the equivalence …

GPUs have emerged as general-purpose accelerators in high-performance computing
(HPC) and scientific applications. However, the reliability characteristics of GPU applications …

Non-negative matrix factorization (NMF) condenses high-dimensional data into lower-
dimensional models subject to the requirement that data can only be added, never …

Background In the last few years, the Non-negative Matrix Factorization (NMF) technique
has gained a great interest among the Bioinformatics community, since it is able to extract …

The holistic analysis and understanding of the latent (that is, not directly observable)
variables and patterns buried in large datasets is crucial for data-driven science, decision …

In this paper we examine how graphic hardware can be used for real-time FIR filtering. We
implement uniformly-partitioned fast convolution in the frequency-domain and evaluate its …

Traditional audio editing tools do not facilitate the task of separating a single mixture
recording (eg pop song) into its respective sources (eg drums, vocal, etc.). Such ability …

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension
reduction used in a wide range of applications, including graph mining, recommender …

We describe the implementation of monaural audio source separation algorithms in our
toolkit openBliSSART (Blind Source Separation for Audio Recognition Tasks). To our …

Due to the popularity of nonnegative matrix factorization and the increasing availability of
massive data sets, researchers are facing the problem of factorizing large-scale matrices of …