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.

翻译：真实数据常以多个不完整视角的形式呈现。不完整多视角聚类是整合这些不完整视角的有效方法。以往方法仅学习不同视角间的一致性信息，忽略了各视角的独有信息，这限制了其聚类性能与泛化能力。为克服这一局限，我们提出了一种新颖的视角变异与视角遗传方法（V3H）。受遗传学中变异与遗传现象的启发，V3H首先将每个子空间分解为对应视角的变异矩阵和所有视角的遗传矩阵，分别表征独有信息与一致性信息。其次，通过基于聚类指示矩阵对齐不同视角，V3H整合来自不同视角的独有信息以提升聚类性能。最后，借助基于遗传矩阵的可调低秩表示，V3H恢复潜在的真实数据结构，从而降低大规模不完整性的影响。更为重要的是，V3H可能是首个将遗传学引入聚类算法的工作，实现了从不完整多视角数据中同步学习一致性信息与独有信息。在十五个基准数据集上的广泛实验验证了其相较于其他当前最优方法的优越性。