Multi-view clustering method based on anchor graph has been widely concerned due to its high efficiency and effectiveness. In order to avoid post-processing, most of the existing anchor graph-based methods learn bipartite graphs with connected components. However, such methods have high requirements on parameters, and in some cases it may not be possible to obtain bipartite graphs with clear connected components. To end this, we propose a label learning method based on tensor projection (LLMTP). Specifically, we project anchor graph into the label space through an orthogonal projection matrix to obtain cluster labels directly. Considering that the spatial structure information of multi-view data may be ignored to a certain extent when projected in different views separately, we extend the matrix projection transformation to tensor projection, so that the spatial structure information between views can be fully utilized. In addition, we introduce the tensor Schatten $p$-norm regularization to make the clustering label matrices of different views as consistent as possible. Extensive experiments have proved the effectiveness of the proposed method.

翻译：基于锚图的 multi-view 聚类方法因其高效性和有效性而受到广泛关注。为避免后处理，现有大多数基于锚图的方法学习具有连通分量的二部图。然而，此类方法对参数要求较高，且在某些情况下可能无法获得具有清晰连通分量的二部图。为此，我们提出一种基于张量投影的标签学习方法（LLMTP）。具体而言，该方法通过正交投影矩阵将锚图投影到标签空间，从而直接获取聚类标签。考虑到不同视角分别投影时，多视角数据的空间结构信息可能在某种程度上被忽略，我们将矩阵投影变换扩展为张量投影，使得视角间的空间结构信息得以充分利用。此外，我们引入张量 Schatten $p$-范数正则化，使不同视角的聚类标签矩阵尽可能一致。大量实验证明了所提方法的有效性。