MapReduce is emerging as a generic parallel programming paradigm for large clusters of
machines. This trend combined with the growing need to run machine learning (ML) …

The Web abounds with dyadic data that keeps increasing by every single second. Previous
work has repeatedly shown the usefulness of extracting the interaction structure inside …

Embodiments include a hardware accelerator for non-nega tive matrix factorization (NMF), in
particular for driving an OLED display. The hardware accelerator determines a oair of factor …

The main task of environmental and geoscience applications is efficient and accurate
quantitative classification of earth surfaces and spatial phenomena. In the past decade …

Nonnegative matrix factorization (NMF) has been successfully applied in several data
mining tasks. Recently, there is an increasing interest in the acceleration of NMF, due to its …

Nonnegative matrix factorization provides a new sight into the observed signals and has
been extensively applied in face recognition, text mining and spectral data analysis. Despite …

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

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 …

Visual feature learning, which aims to construct an effective feature representation for visual
data, has a wide range of applications in computer vision. It is often posed as a problem of …

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