LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.
LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.
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DOI：
10.1145/3394486.3403213
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发表时间：
2020-08
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影响因子：
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通讯作者：
Sun J
中科院分区：
文献类型：
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作者：
Yin K;Afshar A;Ho JC;Cheung WK;Zhang C;Sun J
具有单级缺失值的二进制数据在现实世界应用中无处不在。它们可以由一个不规则的张量在一个维度中变化的不规则张量，其中一个值是指特征的存在，而零表示未知（即存在或不存在特征）。从这种二进制不规则张力张者中学习准确的低级近似值是一项艰巨的任务。但是，没有开发出用于分解不规则张量的现有模型将缺失值考虑在内，并且它们假设高斯分布，在应用于二进制数据时会导致分布不匹配。在本文中，我们提出了Logistic Parafac2（LOGPAR），通过用底层实值张量参数化的Bernoulli分布来对二进制不规则张量进行建模。然后，我们近似具有正标记的学习损失函数的基础张量，以说明缺失值。我们还结合了独特性和时间平滑度正则化，以增强可解释性。使用大型现实世界数据集的广泛实验表明，LogPar在不规则张量完成和下游预测任务中的表现都优于所有基准。对于不规则张量的完成，与最佳基线相比，LogPar的相对相对改善高达26％。此外，与最先进的PARAFAC2模型相比，LogPar的心力衰竭预测的相对改善平均为13.2％，死亡率预测的相对提高为14％。
Binary data with one-class missing values are ubiquitous in real-world applications. They can be represented by irregular tensors with varying sizes in one dimension, where value one means presence of a feature while zero means unknown (i.e., either presence or absence of a feature). Learning accurate low-rank approximations from such binary irregular tensors is a challenging task. However, none of the existing models developed for factorizing irregular tensors take the missing values into account, and they assume Gaussian distributions, resulting in a distribution mismatch when applied to binary data. In this paper, we propose Logistic PARAFAC2 (LogPar) by modeling the binary irregular tensor with Bernoulli distribution parameterized by an underlying real-valued tensor. Then we approximate the underlying tensor with a positive-unlabeled learning loss function to account for the missing values. We also incorporate uniqueness and temporal smoothness regularization to enhance the interpretability. Extensive experiments using large-scale real-world datasets show that LogPar outperforms all baselines in both irregular tensor completion and downstream predictive tasks. For the irregular tensor completion, LogPar achieves up to 26% relative improvement compared to the best baseline. Besides, LogPar obtains relative improvement of 13.2% for heart failure prediction and 14% for mortality prediction on average compared to the state-of-the-art PARAFAC2 models.