小球和大边缘支张量机用于不平衡的张量数据分类
Hexuan Liu1, Xiao Li2, Yitian Xu1
1College of Science, China Agricultural University, Beijing, 100083, China.
概括
一个新的小球和大边缘支张量机 (SSLMSTM) 有效地对不平衡的张量数据进行分类. 这种方法延伸到更高的等级 (HR-SSLMSTM),性能优于现有的方法.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 不平衡的数据分类具有挑战性,特别是在张量数据中.
- 现有的方法,如小球和大边缘方法 (SSLM),仅限于向量数据.
研究的目的:
- 为不平衡张量数据分类提出一种新型模型.
- 为了提高分类,利用张量数据中固有的结构信息.
主要方法:
- 小球和大边缘支张力机 (SSLMSTM) 的介绍.
- 由一个等级-1张量中心的两个同心的超球的构造.
- 扩展到更高级别的R案例 (HR-SSLMSTM).
- 使用CANDECOMP/PARAFAC分解和交替代来解决模型.
主要成果:
- SSLMSTM有效地捕捉在一个小的超球范围内正常的样本,并将异常物推到一个大的超球之外.
- 超球之间的边际增加提高了性能.
- 实验证明了SSLMSTM和HR-SSLMSTM的有效性和有效性.
结论:
- SSLMSTM和HR-SSLMSTM为不平衡的张量数据分类提供了强大的解决方案.
- 拟议的模型成功地利用张量数据结构,以获得卓越的性能.
- 这项工作为机器学习中处理复杂,不平衡的数据集开辟了新的途径.
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