Multiview clustering has been extensively studied to take advantage of multi-source information to improve the clustering performance. In general, most of the existing works typically compute an n * n affinity graph by some similarity/distance metrics (e.g. the Euclidean distance) or learned representations, and explore the pairwise correlations across views. But unfortunately, a quadratic or even cubic complexity is often needed, bringing about difficulty in clustering largescale datasets. Some efforts have been made recently to capture data distribution in multiple views by selecting view-wise anchor representations with k-means, or by direct matrix factorization on the original observations. Despite the significant success, few of them have considered the view-insufficiency issue, implicitly holding the assumption that each individual view is sufficient to recover the cluster structure. Moreover, the latent integral space as well as the shared cluster structure from multiple insufficient views is not able to be simultaneously discovered. In view of this, we propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space with diverse adaptive contributions. Meanwhile, a centroid representation with orthogonality constraint and cluster partition are seamlessly constructed to approximate the latent integral space. An alternate minimizing algorithm is developed to solve the optimization problem, which is proved to have linear time complexity w.r.t. the sample size. Extensive experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method compared with the state-of-the-art methods.

提出了一种自适应加权积分空间 (AIMC) 方法，用于快速多视图聚类，该方法具有近乎线性时间复杂度，通过视图生成模型和质心表示构造近似的潜在积分空间和聚类划分，通过多个视图发现了潜在整体空间和共享聚类结构，通过实验验证了该方法的卓越性能。

自适应加权积分空间用于快速多视角聚类