离线和在线联张量分解与知识图
SeungJoo Lee1, Yong-Chan Park2, U Kang2
1IPAI, Seoul National University, Seoul, Republic of Korea.
PloS one
|November 12, 2025
概括
我们介绍了基于知识图的联张量分解 (KG-CTF) 和其在线版本 (OKG-CTF) 来分析不规则张量. 这些方法有效地将静态知识图信息与动态时间数据相结合,提高了准确性和速度.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 张量分解 张量分解
背景情况:
- PARAFAC2分解分析不规则张量,但往往忽略了知识图等静态特征.
- 现有的方法专注于动态的时间特征,忽视了对全面分析至关重要的时间不变信息.
- 由于时间变化,不规则张量在现实数据中经常出现.
研究的目的:
- 提出新的张量分解方法,以捕捉不规则张量中的动态和静态特征.
- 将知识图信息作为静态特征集成到张量分解中.
- 开发离线 (KG-CTF) 和在线流 (OKG-CTF) 解决方案,用于合张量分解.
主要方法:
- 开发了KG-CTF和OKG-CTF,通过共享轴将不规则的时间张量与知识图张量相合.
- 使用关系规范化来维持知识图因子矩阵中的结构依赖.
- 利用基于动量的更新策略来加速因子矩阵的趋同.
主要成果:
- 与现有的PARAFAC2方法相比,KG-CTF在离线设置中显示出高达1.64倍的错误率降低.
- OKG-CTF实现了高达5.7×比现有的流式方法更快的运行时间.
- 这两种方法都成功地将静态知识图特征与动态时间数据相结合.
结论:
- 通过结合知识图,KG-CTF和OKG-CTF为分析不规则张量提供了有效的解决方案.
- 这些合张量分解方法在线和线下流媒体场景中提高了准确性和效率.
- 静态和动态特征的整合为复杂的时间数据提供了更完整的理解.
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