EBMGC-GNF:通过善邻融合实现高效平衡多视图图集群
概括
本研究介绍了一种通过良邻融合 (EBMGC-GNF) 模型的高效平衡多视图集群. 这种新的方法通过有效地融合邻近信息和平衡集群属性以获得卓越的性能来增强多视图图表集群.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 多视图图表集群对于在数据集中利用一致的结构信息至关重要.
- 现有的方法往往难以有效地从多个图表视图中提取和融合可信的邻居信息.
- 实现平衡的集群属性对于适应各种数据分布至关重要.
研究的目的:
- 通过良邻融合 (EBMGC-GNF) 模型提出一个高效平衡的多视图图集群.
- 通过交叉查看好邻居投票模块,全面提取可信的一致邻居信息.
- 引入一种新的平衡规范化术语,以适应不同的数据分布.
主要方法:
- EBMGC-GNF模型使用交叉视图好邻居投票模块来提取邻居信息.
- 基于p-功率函数的新型平衡规范化术语用于调整集群平衡.
- 使用图形粗化和加速坐标下降算法有效地解决了优化问题.
主要成果:
- 广泛的实验结果证明了拟议的EBMGC-GNF模型的有效性.
- 该模型在大多数场景中显示出与最先进的方法相比,性能优越.
- 建议方法的有效性和效率都得到了验证.
结论:
- EBMGC-GNF模型为多视图图表集群提供了一个有效的解决方案.
- 交叉查看好邻居投票模块和平衡的规范化期限显著提高了集群性能.
- 拟议的方法为复杂的多视图图表集群任务提供了一种高效和有效的方法.
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