Label Learning Method Based on Tensor Projection
This work addresses a specific bottleneck in multi-view clustering for data analysis applications, representing an incremental improvement over existing methods.
The paper tackles the problem of parameter sensitivity and unclear connected components in existing anchor graph-based multi-view clustering methods by proposing a label learning method based on tensor projection (LLMTP), which directly obtains cluster labels through orthogonal projection and uses tensor Schatten p-norm regularization to improve consistency across views, with extensive experiments proving its effectiveness.
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.