论文标题
V3H：查看变化和查看遗传因素不完整的多视图集群
V3H: View Variation and View Heredity for Incomplete Multi-view Clustering
论文作者
论文摘要
实际数据通常以多个不完整视图的形式出现。不完整的多视图聚类是整合这些不完整视图的有效方法。以前的方法仅了解不同视图之间的一致信息，并忽略每个视图的独特信息，这限制了它们的聚类性能和概括。为了克服这一限制，我们提出了一种新颖的观点变化，并查看了遗传方法（V3H）。受遗传学的变化和遗传性的启发，V3H首先将每个子空间分解为相应视图的变体矩阵和所有视图的遗传矩阵，以分别表示独特的信息和一致的信息。然后，通过根据其群集指示矩阵对齐不同的视图，V3H将来自不同视图的唯一信息集成了以提高群集性能。最后，借助基于遗传矩阵的可调低级别表示，V3H恢复了基本的真实数据结构，以减少大型不完整的影响。更重要的是，V3H可能提出了第一批引入遗传学的工作，以共同学习一致的信息以及来自不完整的多视图数据的独特信息，以聚集算法。 15个基准数据集的广泛实验结果验证了其优越性比其他最先进的实验性。
Real data often appear in the form of multiple incomplete views. Incomplete multi-view clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V3H). Inspired by the variation and the heredity in genetics, V3H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V3H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V3H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V3H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multi-view data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts.
