Abstract: Matrix factorization is a fundamental characterization model in machine learning and is usually solved using mathematical decomposition reconstruction loss. However, matrix factorization is ...

Modern electron microscopy enables the acquisition of extremely large datasets, necessitating optimized machine learning techniques, such as dimensionality reduction and clustering, to extract ...

Classical computing has borne witness to the development of machine learning. The integration of quantum technology into this mix will lead to unimaginable benefits and be regarded as a giant leap ...

Network modeling is a powerful alternative in computational biology for deciphering valuable and novel insights into complex molecular interactions and functions of biological systems (Guzzi and Roy, ...

Sequencing of cell-free DNA (cfDNA) is currently being used to detect cancer by searching both for mutational and non-mutational alterations. Recent work has shown that the length distribution of ...

NMF solvers written by MATLAB, appplication MATLAB flies using NMF solvers, and your comments and suggestions. C.-J. Lin, "On the convergence of multiplicative update algorithms for nonnegative matrix ...

Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe–disease associations (MDAs) can help reveal the pathological ...

Topic clusters and recommender systems can help SEO experts to build a scalable internal linking architecture. And as we know, internal linking can impact both user experience and search rankings.

NMFk is a module of the SmartTensors ML framework (smarttensors.com). NMFk is a novel unsupervised machine learning methodology that allows for the automatic identification of the optimal number of ...

Abstract: A novel unsupervised machine learning algorithm for single channel source separation is presented. The proposed method is based on nonnegative matrix factorization, which is optimized under ...