摘要
针对具有极度稀疏和不平衡的非负不完整数据的填补问题,文中提出了一种非线性表征的概率潜在因子张量模型。通过合理假设数据的概率分布作为先验信息,缓解了数据的稀疏性。利用非线性映射实现对数据中每一非负元素的非线性表征,提高了模型的表征能力。考虑到数据的不平衡性,对传统正则化项添加基于实例频率的权重,增加了正则化项的有效性和针对性。实验结果表明,所提模型在补全精度和时间成本方面较现有模型具有明显提升。
In view of the filling problem of non-negative incomplete data with extremely sparse and unbalanced data,a probabilistic potential factor tensor model is proposed.The data sparsity is mitigated by reasonably assuming the probability distribution of the data as a priori information.Nonlinear mapping is used to realize nonlinear characterization of each non-negative element in the data and improve the characterization ability of the model.Considering the unbalance of data,the weights based on instance frequency are added to the traditional regularization terms to increase the effectiveness and pertinence of regularization terms.The experimental results show that the proposed model has obvious improvement over the existing model in terms of completion accuracy and time cost.
作者
董佳英
宋燕
李明
DONG Jiaying;SONG Yan;LI Ming(College of Science,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Computer Engineering,Jiangsu Ocean University,Lianyungang 222005,China)
出处
《电子科技》
2025年第3期7-15,共9页
Electronic Science and Technology
基金
国家自然科学基金(62073223)
上海市自然科学基金(22ZR1443400)。
关键词
非线性表征
概率潜在因子张量模型
实例频率
非线性映射
数据稀疏性
CP分解
不平衡分布
正则项
nonlinear representation
probabilistic factorization tensor model
frequency of known entries
nonlinear mapping
data sparsity
CP decomposition
unbalanced distribution
regular term
