论文标题
通过动态图结构学习半监督聚类
Semi-Supervised Clustering via Dynamic Graph Structure Learning
论文作者
论文摘要
大多数现有的半监督基于图的聚类方法通过完善亲和力矩阵或直接限制数据点的低维表示来利用监督信息。亲和力矩阵代表图形结构，对于半监督基于图的聚类的性能至关重要。但是，现有方法采用静态亲和力矩阵来学习数据点的低维表示，并且在学习过程中不会优化亲和力矩阵。在本文中，我们提出了一种新型的动态图结构学习方法，用于半监督聚类。在这种方法中，我们通过利用给定的成对约束来同时优化数据点的亲和力矩阵和低维表示。此外，我们提出了一种交替的最小化方法，并通过经过验证的收敛来解决所提出的非凸模型。在迭代过程中，我们的方法循环更新数据点的低维表示并完善了亲和力矩阵，从而导致动态亲和力矩阵（图结构）。具体而言，对于更新亲和力矩阵，我们使用具有明显不同的低维表示的数据点具有为0的亲和力值。此外，我们通过在数据点之间整合局部距离和全局自我代表来构建初始亲和力矩阵。在不同设置下的八个基准数据集上的实验结果显示了所提出方法的优势。
Most existing semi-supervised graph-based clustering methods exploit the supervisory information by either refining the affinity matrix or directly constraining the low-dimensional representations of data points. The affinity matrix represents the graph structure and is vital to the performance of semi-supervised graph-based clustering. However, existing methods adopt a static affinity matrix to learn the low-dimensional representations of data points and do not optimize the affinity matrix during the learning process. In this paper, we propose a novel dynamic graph structure learning method for semi-supervised clustering. In this method, we simultaneously optimize the affinity matrix and the low-dimensional representations of data points by leveraging the given pairwise constraints. Moreover, we propose an alternating minimization approach with proven convergence to solve the proposed nonconvex model. During the iteration process, our method cyclically updates the low-dimensional representations of data points and refines the affinity matrix, leading to a dynamic affinity matrix (graph structure). Specifically, for the update of the affinity matrix, we enforce the data points with remarkably different low-dimensional representations to have an affinity value of 0. Furthermore, we construct the initial affinity matrix by integrating the local distance and global self-representation among data points. Experimental results on eight benchmark datasets under different settings show the advantages of the proposed approach.
