对于多视图子空间集群的双核张量Schatten-p规范最小化.
概括
本研究介绍了多视图子空间集群的双核张量Schatten-p规范最小化 (BTMSC),比传统的低级别表示方法提高了性能和效率. BTMSC有效地处理高阶相关性和子空间结构,以更好地表示数据.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机视觉 计算机视觉
背景情况:
- 多视图子空间集群集成来自不同数据源的信息.
- 低级别表示 (LRR) 是一个基准,但受到性能限制和高计算成本的影响.
- 现有的LRR方法通常使用有偏见的核规范,并且涉及计算上昂贵的单值分解 (SVD).
研究的目的:
- 解决多视图子空间集群中现有的低级别表示方法的局限性.
- 提出一种新的方法,以提高集群性能和计算效率.
- 在多视图特征中探索高阶相关性和子空间结构.
主要方法:
- 建议用于多视图子空间集群的双核张量Schatten-p规范最小化 (BTMSC).
- 构建了一个第三阶张量,以捕捉高阶的相关性和子空间结构.
- 利用双核准常态 (BiN) 分因式来实现高效的张量分解.
- 开发了一个交替优化算法来解决BTMSC模型.
主要成果:
- 与最先进的方法相比,BTMSC在十个文本和图像数据集中表现出更高的性能.
- 拟议的方法有效地捕捉了在内视和内视维度中的低级属性.
- 通过将张量分解成较小的组件,BiN因数分解显著提高了计算效率.
结论:
- 在多视图子空间集群中,BTMSC提供了显著的进步.
- 该方法克服了传统LRR方法的局限性,提供了更高的准确性和效率.
- 基于张量的BTMSC方法有效地利用多视图数据来改善聚类结果.
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