In data analysis and machine learning practice, "dimensionality reduction" is an essential technique for visualizing high-dimensional data and as a preprocessing step for clustering. Representative ...

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...

Temporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-lasting trends and transient topics. Effective topic modeling strategies should detect both types and ...

Singular Value Decomposition (SVD) is a powerful technique in linear algebra with numerous applications in data science, such as dimensionality reduction, image compression, and noise reduction. NumPy ...

NonnegMFPy ----- NonnegMFPy is developed and maintained by Guangtun Ben Zhu, It is designed to solve nonnegative matrix factorization (NMF) given a dataset with ...

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional ...

To get a better understanding of electromagnetics and free myself from the commercial EM software, I’ve been trying to make my own antenna simulator in the format Notebook using Python [1]. The ...

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 ...

The chemical purity of materials is important for semiconductors, including the carbon nanotube material system, which is emerging in semiconductor applications. One approach to get statistically ...

Many studies have indicated miRNAs lead to the occurrence and development of diseases through a variety of underlying mechanisms. Meanwhile, computational models can save time, minimize cost, and ...