论文标题
使用内核和变异推理的大规模张量回归
Large Scale Tensor Regression using Kernels and Variational Inference
论文作者
论文摘要
当将潜在因子表示为侧面信息的函数并提出一种减轻这种弱点的新方法时，我们概述了张量分解模型的固有弱点。我们将我们的方法\ textIt {kernel炸量}（kft）造成，并将其作为大型预测工具，用于高维数据。我们的结果显示了\ textIt {lightGBM}和\ textit {field inate actionalsization Machines}（FFM）的卓越性能，这是两种算法，具有广泛的记录，广泛用于工业预测中。我们还为KFT开发了一个变异推理框架，并将我们的预测与三个大型数据集的校准不确定性估计相关联。此外，根据常数和高斯噪声，KFT在经验上被证明是对非信息侧信息的鲁棒性。
We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against \textit{LightGBM} and \textit{Field Aware Factorization Machines}(FFM), two algorithms with proven track records widely used in industrial forecasting. We also develop a variational inference framework for KFT and associate our forecasts with calibrated uncertainty estimates on three large scale datasets. Furthermore, KFT is empirically shown to be robust against uninformative side information in terms of constants and Gaussian noise.
