Sun Shiliang, Zhang Nan
IEEE Trans Neural Netw Learn Syst. 2024 Mar;35(3):4017-4031. doi: 10.1109/TNNLS.2022.3201562. Epub 2024 Feb 29.
Despite incomplete multiview clustering (IMC) being widely studied in the past decade, it is still difficult to model the correlation among multiple views due to the absence of partial views. Most existing works for IMC only mine the correlation among multiple views from available views and ignore the importance of missing views. To address this issue, we propose a novel Incomplete Multiview Nonnegative representation learning model with Graph completion and Adaptive neighbors (IMNGA), which performs common graph learning, missing graph completion, and consensus nonnegative representation learning simultaneously. In IMNGA, the common graph on all views and the incomplete graph of each view are used to reconstruct the completed graph of the corresponding view, where the common graph satisfies the neighbor constraints of incomplete multiview data and consensus representation. IMNGA gets consensus representation by factorizing completed and incomplete graphs, where consensus representation satisfies the common graph constraint. IMNGA shows its effectiveness by outperforming other state-of-the-art methods.
尽管过去十年中不完全多视图聚类（IMC）得到了广泛研究，但由于部分视图缺失，对多视图之间的相关性进行建模仍然很困难。大多数现有的IMC工作仅从可用视图中挖掘多视图之间的相关性，而忽略了缺失视图的重要性。为了解决这个问题，我们提出了一种新颖的具有图补全和自适应邻居的不完全多视图非负表示学习模型（IMNGA），该模型同时执行通用图学习、缺失图补全和一致性非负表示学习。在IMNGA中，所有视图上的通用图和每个视图的不完全图用于重建相应视图的完整图，其中通用图满足不完全多视图数据的邻居约束和一致性表示。IMNGA通过对完整图和不完全图进行因式分解来获得一致性表示，其中一致性表示满足通用图约束。IMNGA通过优于其他现有先进方法展示了其有效性。
