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Fast Clustering by Directly Solving Bipartite Graph Clustering Problem.
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.4 ) Pub Date : 2022-11-17 , DOI: 10.1109/tnnls.2022.3219131
Feiping Nie , Jingjing Xue , Rong Wang , Liang Zhang , Xuelong Li

Spectral clustering (SC) has been widely used in many applications and shows excellent performance. Its high computational cost limits its applications; many strategies including the anchor technique can partly alleviate the high computational cost problem. However, early methods ignore the fact that SC usually involves two stages: relaxation and postprocessing, i.e., it relaxes the discrete constraints to continuous constraints, and then conducts the postprocessing to get the discrete solution, which is time-consuming and deviates from directly solving the primal problem. In this article, we first adopt the bipartite graph strategy to reduce the time complexity of SC, and then an improved coordinate descent (CD) method is proposed to solve the primal problem directly without singular value decomposition (SVD) and postprocessing, i.e., directly solving the primal problem not approximately solving. Experiments on various real-world benchmark datasets show that the proposed method can get better solutions faster with better clustering performance than traditional optimization methods. Furthermore, it can jump out of local minima of traditional methods and continue to obtain better local solutions. Moreover, compared with other clustering methods, it also shows its superiority.

中文翻译:

通过直接解决二分图聚类问题的快速聚类。

谱聚类(SC)已在许多应用中得到广泛应用并表现出优异的性能。其高计算成本限制了其应用;包括锚技术在内的许多策略可以部分缓解高计算成本问题。然而,早期的方法忽略了SC通常涉及两个阶段:松弛和后处理,即将离散约束松弛为连续约束,然后进行后处理得到离散解,耗时且偏离直接求解最初的问题。在本文中,我们首先采用二分图策略来降低SC的时间复杂度,然后提出一种改进的坐标下降(CD)方法来直接求解原始问题,无需奇异值分解(SVD)和后处理,即 直接解决原始问题而不是近似解决。在各种真实世界基准数据集上的实验表明,与传统的优化方法相比,所提出的方法可以更快地获得更好的解决方案,并且具有更好的聚类性能。此外,它可以跳出传统方法的局部极小值,继续获得更好的局部解。而且,与其他聚类方法相比,它也显示出它的优越性。
更新日期:2022-11-17
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