Sparse representation and cooperative learning are two representative technologies in the field of multi-view spectral clustering. The former can effectively extract features of multiple views by the removal of redundant information contained in each view. The latter can incorporate the diversity of each view. However, traditional sparse representation and cooperative learning algorithms are inadequate in preserving the internal geometric features of data by manifold regularization. In fact, general approaches rarely consider the similarities between the internal graph structures of individual views. Moreover, to achieve the optimal global feature learning, we present a novel two-step multi-view spectral clustering strategy, which combines the proposed sparse representation by adaptive graph learning with adaptive weighted cooperative learning. In the first step, the proposed matrix factorization by manifold regularization can strengthen the sparse features clustering discrimination of samples of each view. Specifically, the synchronization optimization method by introducing adaptive graph learning can better retain its internal complete structure of each view. This ensures the structure correlation of views through the usage of the sparse matrix and the optimal graph similarity matrix. In the second step, the adaptive weighted cooperative learning is performed on each view to get a global optimized matrix. In order to ensure that the global matrix is associated with various view features, graph learning is also performed on the global matrix. Experiment results on several multi-view datasets and single-view datasets show that the proposed method significantly outperformed the state-of-the-art algorithms.

稀疏表示和协作学习是多视图谱聚类领域中的两种代表性技术。前者可以通过删除每个视图中包含的冗余信息来有效地提取多个视图的特征。后者可以合并每个视图的多样性。但是，传统的稀疏表示和协作学习算法不足以通过流形规则化来保存数据的内部几何特征。实际上，一般方法很少考虑单个视图的内部图结构之间的相似性。此外，为了实现最佳的全局特征学习，我们提出了一种新颖的两步多视图谱聚类策略，该策略将通过自适应图学习提出的稀疏表示与自适应加权合作学习相结合。第一步，通过流形正则化提出的矩阵分解可以增强每个视图样本的稀疏特征聚类判别。具体而言，通过引入自适应图学习的同步优化方法可以更好地保留其每个视图的内部完整结构。这样可以通过使用稀疏矩阵和最佳图相似度矩阵来确保视图的结构相关性。在第二步中，对每个视图执行自适应加权合作学习，以获得全局优化矩阵。为了确保全局矩阵与各种视图特征相关联，还对全局矩阵执行了图学习。