Minimum Distribution Support Vector Clustering

Wang, Yan; Chen, Jiali; Xie, Xuping; Yang, Sen; Pang, Wei; Huang, Lan; Zhang, Shuangquan; Zhao, Shuhong

doi:10.3390/e23111473

Cited by 2 publications

(2 citation statements)

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“…A dual problem solver is the core of the training phase in which complex operations, huge iterations, and unaffordable memory by the pre-computed kernel matrix are critical and relevant tasks. To improve the robustness, [9] characterized the optimal sphere by introducing the first-order and second-order statistics. For complexity reduction, [4] rewrote the dual problem by introducing the Jaynes maximum entropy, while [35] started the solver with a position-based weight.…”

Section: Related Workmentioning

confidence: 99%

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Improved Boundary Support Vector Clustering with Self-Adaption Support

Ping

Hao³

et al. 2022

Electronics

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Concerning the good description of arbitrarily shaped clusters, collecting accurate support vectors (SVs) is critical yet resource-consuming for support vector clustering (SVC). Even though SVs can be extracted from the boundaries for efficiency, boundary patterns with too much noise and inappropriate parameter settings, such as the kernel width, also confuse the connectivity analysis. Thus, we propose an improved boundary SVC (IBSVC) with self-adaption support for reasonable boundaries and comfortable parameters. The first self-adaption is in the movable edge selection (MES). By introducing a divide-and-conquer strategy with the k-means++ support, it collects local, informative, and reasonable edges for the minimal hypersphere construction while rejecting pseudo-borders and outliers. Rather than the execution of model learning with repetitive training and evaluation, we fuse the second self-adaption with the flexible parameter selection (FPS) for direct model construction. FPS automatically selects the kernel width to meet a conformity constraint, which is defined by measuring the difference between the data description drawn by the model and the actual pattern. Finally, IBSVC adopts a convex decomposition-based strategy to finish cluster checking and labeling even though there is no prior knowledge of the cluster number. Theoretical analysis and experimental results confirm that IBSVC can discover clusters with high computational efficiency and applicability.

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Section: Related Workmentioning

confidence: 99%

“…On the other hand, parameter selection stops model training at the right time for cluster discovery. In the literature [1,9], the expected cluster number k is still the most common stop condition before obtaining the appropriate kernel width q by an incremental test. However, k is what we need for discovery in practical situations [8,10].…”

Section: Introductionmentioning

confidence: 99%