Concept Factorization (CF), as a novel paradigm of representation learning,
has demonstrated superior performance in multi-view clustering tasks. It
overcomes limitations such as the non-negativity constraint imposed by
traditional matrix factorization methods and leverages kernel methods to learn
latent representations that capture the underlying structure of the data,
thereby improving data representation. However, existing multi-view concept
factorization methods fail to consider the limited labeled information inherent
in real-world multi-view data. This often leads to significant performance
loss. To overcome these limitations, we propose a novel semi-supervised
multi-view concept factorization model, named SMVCF. In the SMVCF model, we
first extend the conventional single-view CF to a multi-view version, enabling
more effective exploration of complementary information across multiple views.
We then integrate multi-view CF, label propagation, and manifold learning into
a unified framework to leverage and incorporate valuable information present in
the data. Additionally, an adaptive weight vector is introduced to balance the
importance of different views in the clustering process. We further develop
targeted optimization methods specifically tailored for the SMVCF model.
Finally, we conduct extensive experiments on four diverse datasets with varying
label ratios to evaluate the performance of SMVCF. The experimental results
demonstrate the effectiveness and superiority of our proposed approach in
multi-view clustering tasks.

本篇研究通过提出一个新的半监督多视图概念分解模型 SMVCF，结合多视图概念分解、标签传播和流形学习等技术，以及引入适应性权重向量，来克服传统方法中忽略标签和视角信息的不足，从而在多视图聚类任务中取得了令人满意的效果。