Recent advances in multi-view multi-label learning are often hampered by the prevalent challenges of incomplete views and missing labels, common in real-world data due to uncertainties in data collection and manual annotation. These challenges restrict the capacity of the model to fully utilize the diverse semantic information of each sample, posing significant barriers to effective learning. Despite substantial scholarly efforts, many existing methods inadequately capture the depth of semantic information, focusing primarily on shallow feature extractions that fail to maintain semantic consistency. To address these shortcomings, we propose a novel Deep semantic structure-preserving (SSP) model that effectively tackles both incomplete views and missing labels. 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.

多视图多标签学习的最新进展常常受到视图不完整和标签缺失的普遍挑战的阻碍，由于数据收集和手动注释的不确定性，这些挑战在现实世界数据中很常见。这些挑战限制了模型充分利用每个样本的多样化语义信息的能力，对有效学习构成了重大障碍。尽管学术上付出了大量努力，但许多现有方法不足以捕获语义信息的深度，主要关注无法保持语义一致性的浅层特征提取。为了解决这些缺点，我们提出了一种新颖的深度语义结构保留（SSP）模型，可以有效地解决不完整的视图和丢失的标签问题。 SSP 创新地结合了图约束学习（GCL）方案，以确保在不同视图的整个特征提取过程中保留语义结构。此外，SSP还集成了伪标签自定进度学习（PSL）策略来解决经常被忽视的标签丢失问题，在提高分类准确性的同时保留数据的分布结构。 SSP 模型创建了一个统一的框架，协同使用 GCL 和 PSL，在特征提取和分类阶段保持语义结构信息的完整性。对五个真实数据集的广泛评估表明，SSP 方法优于现有方法，包括 lrMMC、MVL-IV、MvEL、iMSF、iMvWL、NAIML 和 DD-IMvMLC-net。它有效地减轻了数据不完整性的影响并增强了语义表示的保真度。