Learning Multiple Networks via Supervised Tensor Decomposition
Learning Multiple Networks via Supervised Tensor Decomposition
复制标题
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Jiaxin Hu;Chanwoo Lee;Miaoyan Wang
中科院分区：
文献类型：
--
作者：
Jiaxin Hu;Chanwoo Lee;Miaoyan Wang
我们考虑的问题，张量分解与多个边信息可作为交互功能。这些问题在神经成像、网络建模和时空分析中很常见。我们开发了一个新的指数张量分解模型，并建立了理论上的精度保证。进一步提出了一种有效的交替优化算法。与早期的方法不同，我们的建议能够处理广泛的数据类型，包括连续，计数和二进制观测。我们将该方法应用于人类连接体项目的扩散张量成像数据，并识别与可用特征相关的关键脑连接模式。我们的方法将帮助从业者有效地分析各个领域的张量数据集。为此，所有数据和代码都可以在https://CRAN.R-project.org/ package=tensorregress上获得。
We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efficient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efficiently analyze tensor datasets in various areas. Toward this end, all data and code are available at https://CRAN.R-project.org/ package=tensorregress .