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SSP innovatively incorporates a graph constraint learning (GCL) scheme to ensure the preservation of semantic structure throughout the feature extraction process across different views. Additionally, the SSP integrates a pseudo-labeling self-paced learning (PSL) strategy to address the often-overlooked issue of missing labels, enhancing the classification accuracy while preserving the distribution structure of data. The SSP model creates a unified framework that synergistically employs GCL and PSL to maintain the integrity of semantic structural information during both feature extraction and classification phases. Extensive evaluations across five real datasets demonstrate that the SSP method outperforms existing approaches, including lrMMC, MVL-IV, MvEL, iMSF, iMvWL, NAIML, and DD-IMvMLC-net. It effectively mitigates the impacts of data incompleteness and enhances semantic representation fidelity.<\/jats:p>","DOI":"10.1007\/s40747-024-01562-5","type":"journal-article","created":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T07:02:41Z","timestamp":1722063761000},"page":"7661-7679","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Incomplete multi-view partial multi-label classification via deep semantic structure preservation"],"prefix":"10.1007","volume":"10","author":[{"given":"Chaoran","family":"Li","sequence":"first","affiliation":[]},{"given":"Xiyin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Pai","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Zhuhong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaohuan","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"issue":"6","key":"1562_CR1","doi-asserted-by":"publisher","first-page":"4260","DOI":"10.1109\/TCYB.2020.3025636","volume":"52","author":"S Hu","year":"2020","unstructured":"Hu S, Shi Z, Ye Y (2020) Dmib: dual-correlated multivariate information bottleneck for multiview clustering. 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