Sparse and Tucker in the Scientific Literature: A Bibliometric Study of Trends and Applications

Abstract

This bibliometric study examines research that explicitly combines sparse methods with Tucker-related techniques. Using PRISMA 2020 as a reporting guide for record identification and selection, we retained 254 journal articles indexed in Scopus and Web of Science and published between 2000 and May 2025. The analysis covers annual publication output, raw citation counts, international collaboration, and keyword co-occurrence using VOSviewer (version 1.6.20) and R (version 4.4.2). The 254 articles show sustained growth in publication output between 2010 and 2024. China and the United States account for a substantial share of the retrieved literature, while Pattern Recognition and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing are prominent within this specific dataset. Because citation counts vary with publication year, discipline, and database coverage, they are interpreted as descriptive indicators rather than direct measures of research quality or normalized impact. The retrieved studies include applications in neuroscience, finance, hyperspectral image analysis, and multimodal learning. The co-occurrence maps center on terms such as tensor decomposition, low-rank approximation, and deep learning. These results describe how sparse and Tucker techniques are being used across several areas of data science, while remaining specific to the corpus defined by the search strategy.