The goal of multi-view clustering is to partition samples into different subsets according to their diverse features. Previous multi-view clustering methods mainly exist two forms: multi-view spectral clustering and multi-view matrix factorization. Although they have shown excellent performance in many occasions, there are still many disadvantages. For example, multi-view spectral clustering usually needs to perform postprocessing. Multi-view matrix factorization directly decomposes the original data features. When the size of features is large, it encounters the expensive time consumption to decompose these data features thoroughly. Therefore, we proposed a novel multi-view clustering approach. The main advantages include the following three aspects: 1) it searches for a common joint graph across multiple views, which fully explores the hidden structure information by utilizing the compatibility among views; 2) the introduced nonnegative constraint manipulates that the final clustering results can be directly obtained; and 3) straightforwardly decomposing the similarity matrix can transform the eigenvalue factorization in spectral clustering with computational complexity O(n3)O(n^{3}) into the singular value decomposition (SVD) with O(nc2)O({n}{c}^{2}) time cost, where n{n} and cc , respectively, denote the numbers of samples and classes. Thus, the computational efficiency can be improved. Moreover, in order to learn a better clustering model, we set that the constructed similarity graph approximates each view affinity graph as close as possible by adding the constraint as the initial affinity matrices own. Furthermore, substantial experiments are conducted, which verifies the superiority of the proposed two clustering methods comparing with single-view clustering approaches and state-of-the-art multi-view clustering methods.

中文翻译：

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通过非负和正交图重建的多视图聚类


多视图聚类的目标是根据样本的不同特征将样本划分为不同的子集。以往的多视点聚类方法主要存在两种形式：多视点谱聚类和多视点矩阵分解。尽管他们在很多场合都表现出了出色的表现，但仍然存在很多缺点。例如，多视图谱聚类通常需要进行后处理。多视图矩阵分解直接分解原始数据特征。当特征量很大时，彻底分解这些数据特征会遇到昂贵的时间消耗。因此，我们提出了一种新颖的多视图聚类方法。主要优点包括以下三个方面：1）跨多个视图搜索公共联合图，利用视图之间的兼容性充分挖掘隐藏的结构信息； 2）引入非负约束，可以直接得到最终的聚类结果； 3）直接分解相似度矩阵可以将计算复杂度为O(n3)O(n^{3})的谱聚类中的特征值分解转化为计算复杂度为O(nc2)O({n}{ c}^{2}) 时间成本，其中 n{n} 和 cc 分别表示样本和类的数量。因此，可以提高计算效率。此外，为了学习更好的聚类模型，我们通过添加初始亲和度矩阵本身的约束来设置构造的相似度图尽可能接近每个视图亲和度图。 此外，还进行了大量实验，验证了所提出的两种聚类方法相对于单视图聚类方法和最先进的多视图聚类方法的优越性。