Label Learning Method Based on Tensor Projection
Jing Li Quanxue Gao Qianqian Wang Cheng Deng Deyan Xie
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.