强大的结构保存张力化表示,用于多视图无监督的特征选择
Chen Wang1, Peng Song1, Changjia Wang1
1School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
概括
本研究介绍了一种强大的结构保存张量化表示 (RSTR) 用于多视图无监督特征选择 (MUFS). RSTR有效地处理噪音数据并优化视图结构,优于现有方法.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机科学 计算机科学
背景情况:
- 自我表示方法为多视图无监督特征选择 (MUFS) 构建亲和矩阵.
- 现有的方法受到数据噪声,因约束而导致的扭曲关系和不理想的视图结构利用的挑战.
研究的目的:
- 为MUFS提出一种新的强大的结构保存张量化表示 (RSTR) 方法.
- 解决现有方法在处理噪声和优化视图结构方面的局限性.
主要方法:
- RSTR将亲和矩阵分解为干净和噪声组件,以减少噪声的影响.
- 为具有物理意义的亲缘关系矩阵和固有结构发现引入了缩放的简单约束.
- 采用等级约束来识别最佳视图结构,并构建加权张量来挖掘视图之间的高阶关系.
主要成果:
- 拟议的RSTR方法与最先进的竞争对手相比,表现优越.
- 在八个数据集上的实验验验证了RSTR在改善MUFS方面的有效性.
结论:
- RSTR为多视图无监督特征选择提供了强大而有效的方法.
- 该方法成功地减轻了噪音,保留了固有的数据结构,并优化了视图表示.
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