Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering accuracy. To this end, we propose a joint learning framework, which is able to learn the graph and the clustering result simultaneously, such that the resulting graph is tailored to the clustering task. The proposed method is formulated as a well-defined nonnegative and off-diagonal constrained optimization problem,which is optimized by an alternative iteration method with the convergence of the value of the objective function guaranteed. The advantage of the proposed model is demonstrated by comparing with 19 state-of-the-art clustering methods on 10 datasets with 4 clustering metrics.

中文翻译：

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聚类感知图的构建：联合学习的视角

基于图的聚类方法已经证明了在各种应用中的有效性。通常，现有的基于图的聚类方法首先构造一个图来表示输入数据，然后对其进行分区以生成聚类结果。但是，这种逐步方式可能使构造的图不适合后续分解的要求，从而导致聚类精度下降。为此，我们提出了一个联合学习框架，该框架能够同时学习图和聚类结果，从而使生成的图适合于聚类任务。将该方法表述为一个定义明确的非负非对角约束优化问题，通过选择迭代方法对其进行优化，并保证目标函数值的收敛。