低级别的离散多视图光谱聚类.
Yu Yun1, Jing Li1, Quanxue Gao1
1School of Telecommunications Engineering, Xidian University, Shaanxi 710071, China.
概括
这项研究引入了一种新的低级别离散多视图光谱聚类模型. 它有效地利用不同视角的互补信息来提高集群性能.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 频谱聚类因其处理复杂数据结构的能力而受欢迎.
- 现有的多视图光谱聚类方法往往忽略了补充信息,并使用了不理想的离散解决方案.
研究的目的:
- 提出一种新的低级别离散多视图光谱聚类模型.
- 通过利用补充信息和整合离散标签回收来解决现有方法的局限性.
主要方法:
- 开发了一个使用张量Schatten p-norm的模型,以利用指示矩阵的互补信息.
- 将低级张量学习和离散标签恢复集成到一个统一的框架中.
- 避免了传统放松和谨慎策略的不确定性.
主要成果:
- 拟议的模型有效地利用跨多个观点的互补信息.
- 综合框架提供了更直接和最佳的离散解决方案.
- 在基准数据集上的实验结果验证了该方法的有效性.
结论:
- 拟议的低级别离散多视图光谱集群模型提供了卓越的性能.
- 这种方法通过利用互补信息和直接离散优化来增强聚类.
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