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The key innovation of GRAB-MDM is a view-dependent bandwidth selection strategy that adapts to the geometry and noise level of each view, enabling a stable and principled construction of multiview diffusion operators. Under a common-manifold model, we establish asymptotic convergence results and show that the adaptive bandwidths lead to provably robust recovery of the shared intrinsic structure, even when noise levels and sensor dimensions differ across views. Numerical experiments and two real data analyses demonstrate that GRAB-MDM significantly improves robustness and embedding quality compared with existing algorithms. The proposed framework offers a practical and theoretically grounded solution for multiview sensor fusion in high-dimensional noisy environments.<\/jats:p>","DOI":"10.1214\/26-aos2664","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T12:56:02Z","timestamp":1790945762000},"page":"2637-2664","source":"Crossref","is-referenced-by-count":2,"title":["Generalized robust adaptive-bandwidth MultiView manifold learning in high dimensions with noise"],"prefix":"10.1214","volume":"54","author":[{"given":"Xiucai","family":"Ding","sequence":"first","affiliation":[{"name":"Department of Statistics, University of California, Davis"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Shen","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0253-3156","authenticated-orcid":false,"given":"Hau-Tieng","family":"Wu","sequence":"additional","affiliation":[{"name":"Courant Institute of Mathematical Sciences, New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"108","reference":[{"key":"17","doi-asserted-by":"crossref","unstructured":"<small>Hotelling, H.<\/small> (1936). 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