{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:06:58Z","timestamp":1758931618051,"version":"3.44.0"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper introduces BCenetTucker, a novel bootstrap-enhanced extension of the CenetTucker model designed to address the instability of sparse support recovery in high-dimensional tensor settings. By integrating mode-specific resampling directly into the penalized tensor decomposition process, BCenetTucker improves the reliability and reproducibility of latent structure estimation without compromising the model\u2032s interpretability. The proposed method is systematically benchmarked against classical CenetTucker, Stability Selection, and Bolasso, using real-world gene expression data from the GSE13159 leukemia dataset. Across multiple stability metrics\u2014including support-size deviation, average Jaccard index, inclusion frequency, proportion of stable support, and Stable Selection Index (SSI)\u2014BCenetTucker consistently demonstrates superior robustness and structural coherence relative to competing approaches. In the real data application, BCenetTucker preserved all essential signals originally identified by CenetTucker while uncovering additional marginal yet reproducible features. The method achieved high reproducibility (Jaccard index = 0.975; support-size deviation = 1.7 genes), confirming its sensitivity to weak but stable signals. The protocol was implemented in the GSparseBoot R library, enabling reproducibility, transparency, and applicability to diverse domains involving structured high-dimensional data. Altogether, these results establish BCenetTucker as a powerful and extensible framework for achieving stable sparse decompositions in modern tensor analytics.<\/jats:p>","DOI":"10.3390\/a18100602","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T10:39:37Z","timestamp":1758883177000},"page":"602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bootstrap-Based Stabilization of Sparse Solutions in Tensor Models: Theory, Assessment, and Application"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0652-9986","authenticated-orcid":false,"given":"Gresky","family":"Guti\u00e9rrez-S\u00e1nchez","sequence":"first","affiliation":[{"name":"Department of Statistics, Faculty of Medicine, University of Salamanca, 37008 Salamanca, Spain"},{"name":"National Secretariat of Science, Technology and Innovation (Senacyt), Ciudad del Saber, Building 205, Panama City 06001, Panama"}]},{"given":"Mar\u00eda Purificaci\u00f3n","family":"Vicente-Galindo","sequence":"additional","affiliation":[{"name":"Department of Statistics, Faculty of Medicine, University of Salamanca, 37008 Salamanca, Spain"},{"name":"Center for Statistical Studies Management, State University of Milagro (UNEMI), University Campus, Guayas (Ecuador), Km. 1.5 to Km. 26, Guayas 091050, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6977-7545","authenticated-orcid":false,"given":"Purificaci\u00f3n","family":"Galindo-Villard\u00f3n","sequence":"additional","affiliation":[{"name":"Department of Statistics, Faculty of Medicine, University of Salamanca, 37008 Salamanca, Spain"},{"name":"Center for Statistical Studies Management, State University of Milagro (UNEMI), University Campus, Guayas (Ecuador), Km. 1.5 to Km. 26, Guayas 091050, Ecuador"},{"name":"Center for Statistical Studies and Research, Escuela Superior Polit\u00e9cnica del Litoral (ESPOL), Gustavo Galindo Campus, Km. 30.5 Perimetral Road, Guayaquil 090112, Ecuador"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor Decompositions and Applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/MSP.2013.2297439","article-title":"Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis","volume":"32","author":"Cichocki","year":"2015","journal-title":"IEEE Signal Process. 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