具有绝对投影尺度的鲁棒多关系学习

Dimitris G. Chachlakis, Yorgos Tsitsikas, E. Papalexakis, Panos P. Markopoulos
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引用次数: 3

摘要

RESCAL是一种基于张量分解的多关系学习方法。同时,RESCAL遵循l2规范公式,可以对外围数据损坏非常敏感。在这项工作中,我们提出了a -RESCAL:基于绝对预测的RESCAL的抗腐败重组。具体来说,我们(i)证明了rank-1 a - rescal可以作为对映二元变量的组合问题,并通过穷举搜索精确求解;(ii)开发一种有效的迭代算法来近似求解秩1 A-RESCAL;(3)利用子空间压缩的方法扩展了一般秩的解。我们在多个基准数据集上的实验研究表明,当处理的数据是标称数据时,A-RESCAL的性能与标准RESCAL非常相似,而在数据损坏的情况下,它的鲁棒性要高得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Multi-Relational Learning With Absolute Projection Rescal
RESCAL is a popular approach for multi-relational learning based on tensor decomposition. At the same time, RESCAL follows a L2-norm formulation that can be very sensitive against outlying data corruptions. In this work, we propose A-RESCAL: a corruption-resistant reformulation of RESCAL based on absolute projections. Specifically, we (i) show that rank-1 A-RESCAL can be cast as a combinatorial problem over antipodal binary variables and solve it exactly by exhaustive search; (ii) develop an efficient iterative algorithm for approximating the solution to rank-1 A-RESCAL; and (iii) extend our solver for general rank by means of subspace deflation. Our experimental studies on multiple benchmark datasets show that A-RESCAL performs quite similarly to standard RESCAL when the processed data are nominal, while it is significantly more robust in the case of data corruption.
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