Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

High-dimensional and incomplete (HDI) data subject to the nonnegativity constraints are
commonly encountered in a big data-related application concerning the interactions among …

High-Dimensional and Incomplete (HDI) data are frequently encountered in various Big
Data-related applications. Despite its incompleteness, an HDI data repository contains rich …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

To address the significant performance degradation of conventional underdetermined blind
source separation algorithms for frequency-hop** (FH) signals under time-frequency (TF) …

Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with
highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery …

Blind image separation based on wavelet transform (WT) enhancement algorithm for the
pulse thermography was introduced as a novel modality to recognize the debonding defects …

Abstract Non-negative Matrix Factorization (NMF) is an effective way to solve the
redundancy of non-negative high-dimensional data. Most of the traditional probability-based …

Mutational signature analysis is a recent computational approach for interpreting somatic
mutations in the genome. Its application to cancer data has enhanced our understanding of …