在高维基基因组数据的格拉斯曼多元组上使用张量融合的多核聚类.
1Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, Guizhou, China; Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.
Methods (San Diego, Calif.)
|October 13, 2024
概括
这项研究引入了一种新的多核聚类方法 (MKCTM),使用格拉斯曼多元组上的张量融合来有效处理高维基因组数据. 通过减少基核中的噪音和冗余,MKCTM提高了集群精度.
科学领域:
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 由于噪音和复杂性,高维基因组数据对传统集群算法提出了重大挑战.
- 现有的多核集群方法可以提高亲和力矩阵的质量,但在高维设置中与错误传播和冗余性作斗争.
- 目前的多核融合策略缺乏有效整合多种核信息的可行性.
研究的目的:
- 开发一种先进的多核聚类方法,能够克服高维基因组数据分析现有方法的局限性.
- 在Grassmann分流器上引入一种新的张量融合策略,以实现强大的内核集成.
- 通过最大限度地提高基础内核之间的共识,同时减轻噪音和冗余性来提高集群性能.
主要方法:
- 提出了一种多核聚类方法,在格拉斯曼多元组 (MKCTM) 上使用张量融合.
- 采用张量低级约束来最大限度地实现集群共识,并消除基核中的噪音/冗余.
- 开发了一个统一的优化模型,集成张量学习和融合,通过有效的代算法解决.
- 在Grassmann多元体上融合了学习基础内核,以生成集群的最终共识矩阵.
主要成果:
- 与12种流行的基线聚类方法相比,MKCTM在10个不同的数据集中表现出卓越的性能.
- 张量聚变方法有效地减少了噪音和冗余性,从而提高了集群精度.
- 该方法通过Grassmann多重融合成功地整合了来自多个基核的信息.
结论:
- MKCTM提供了一个强大的和有效的解决方案,用于集群高维基因组数据,优于现有的方法.
- 关于格拉斯曼元组的提议张量融合策略是多核学习的重大进展.
- 开发的代优化算法有效地解决了集成张量学习和融合模型.
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