Machine learning offers a principled approach for develo** sophisticated, automatic, and
objective algorithms for analysis of high-dimensional and multimodal biomedical data. This …

Nonnegative matrix approximation (NNMA) is a recent technique for dimensionality
reduction and data analysis that yields a parts based, sparse nonnegative representation for …

Data analysis is pervasive throughout business, engineering and science. Very often the
data to be analyzed is nonnegative, and it is often preferable to take this constraint into …

This paper addresses the estimation of the latent dimensionality in nonnegative matrix
factorization (NMF) with the (β)--divergence. The (β)-divergence is a family of cost functions …

In this paper, we propose the use of non-negative matrix factorization (NMF) for image
hashing. In particular, we view images as matrices and the goal of hashing as a randomized …

Ride-hailing service has witnessed a dramatic growth over the past decade but meanwhile
raised various challenging issues, one of which is how to provide a timely and accurate …

The real world image databases such as Flickr are characterized by continuous addition of
new images. The recent approaches for image annotation, ie the problem of assigning tags …

Non-negative matrix factorization (NMF) technique has been recently proposed for
dimensionality reduction. NMF is capable to produce region or part based representations of …

The response of high-energy-density materials to thermal or mechanical insults involves
coupled thermal, mechanical, and chemical processes with disparate temporal and spatial …

The most popular algorithms for Nonnegative Matrix Factorization (NMF) belong to a class of
multiplicative Lee-Seung algorithms which have usually relative low complexity but are …