面向快速多视图聚类的双正交空间锚框学习
Learning Anchor in Dual Orthogonal Space for Fast Multi-view Clustering
把「锚框」从单一正交空间扩展到双正交空间（原始特征空间 + 以共识锚定簇中心为基的空间），并用锚框的聚类指示矩阵直接给出划分，这一思路可迁移到大规模视觉检索/聚类中的低秩锚点近似与子空间互补建模。
原文依据 DOSFMVC conducts anchor learning in dual orthogonal space, aiming at utilizing the complementary information among two spaces in producing anchors with high quality.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论大规模多视图聚类旨在以高效的方式挖掘不同视图之间的互补信息与一致信息。尽管现有方法取得了令人瞩目的性能，但它们仅在单一空间中、借助正交约束或其他约束对多视图数据执行锚框学习，从而导致锚框质量不佳。锚框可以同时出现在更多空间中，而这些空间之间的互补信息能够被用于学习锚框。与此同时，大多数现有工作忽略了以锚定簇中心为基的空间来学习锚框。在本工作中，我们提出面向快速多视图聚类的双正交空间锚框学习（DOSFMVC）。DOSFMVC 在双正交空间中执行锚框学习，旨在利用两个空间之间的互补信息来生成高质量的锚框。DOSFMVC 在锚框学习中引入共识锚定簇中心作为额外空间的基，并引入基于该基的锚框聚类指示。锚框学习与划分被整合进一个统一模型，其中最终的簇分配可直接用作聚类结果。大量实验证实，在多个基准数据集上，我们的方法优于若干最优方法。
英文原文摘要 1342 字符
Large-scale multi-view clustering aims to explore the complementary and consistent information among different views in efficient manner. Despite the impressive performance gained by the existing methods, they just perform anchor learning in a single space with the orthogonal or some other constraints from the multi-view data, leading to undesired anchors. The anchors can simultaneously occur in more spaces and the complementary information among these spaces is able to be adopted for learning anchors. Meanwhile, the space with basis being the anchored cluster center is neglected to learn anchors by most existing works. In this work, we propose learning anchor in Dual Orthogonal Space for Fast Multi-view Clustering (DOSFMVC). DOSFMVC conducts anchor learning in dual orthogonal space, aiming at utilizing the complementary information among two spaces in producing anchors with high quality. DOSMFVC introduces the consensus anchored cluster center as basis of the extra space and clustering indicator of anchors based on this basis in anchor learning. The anchor learning and partition are integrated into a unified model, where the final cluster assignment can be adopted for clustering results. Extensive experiments confirm the superiority of our method compared with some state-of-the-art methods on several benchmark datasets.