用于集群的二维半负数矩阵因数分解
Chong Peng1, Zhilu Zhang1, Chenglizhao Chen1
1College of Computer Science and Technology, Qingdao University.
概括
这项研究引入了一种新的半负数矩阵因子化 (TS-NMF) 方法,用于2D数据. TS-NMF保存空间信息并增强数据表示,以改善集群和现实世界的应用.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 减小尺寸性的减小方法
背景情况:
- 现有的二维数据分解方法往往会丢失关键的空间信息.
- 将二维数据预处理为矢量可以降低其固有的结构.
研究的目的:
- 为2D数据提出一种新的半负数矩阵因子化 (TS-NMF) 方法.
- 为了保留在传统的矢量化方法中丢失的空间信息.
- 增强数据表示,以改善聚类和分析.
主要方法:
- 开发了一种TS-NMF方法,集成投影矩阵搜索,新数据表示构建和多重学习.
- 在预测的子空间中构建适应式分流器,以减轻噪声和异常值.
- 优化投影方向,以集群目标为指导.
主要成果:
- TS-NMF有效地以二维数据表示形式保留空间信息.
- 综合模型产生了强大且具有代表性的数据特征.
- 实验结果表明,与最先进的算法相比,性能优越.
结论:
- 通过保持空间完整性,TS-NMF在分析2D数据方面取得了重大进展.
- 该方法显示了各种各样的现实应用程序的巨大潜力,这些应用程序需要强大的数据表示.
- 投影,表示和多重学习的无集成增强了分析能力.
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