多源非超高斯图模型中差分网络的高效学习
Efficient learning of differential network in multi-source non-paranormal graphical models
AI总结：
本文针对多源非超高斯图模型，提出一种基于 lasso 惩罚 D-trace 损失的高效差分网络学习方法，可输出精确解路径，在合成与癌症耐药性数据上均展现出更高的速度、精度和有效性。
摘要：
本文研究两类非超高斯图模型之间稀疏结构变化或差分网络的学习问题。我们假设每一类都可用一个多源且异质的数据集，其中所有非超高斯图模型的协方差矩阵相同。差分网络由差分精度矩阵编码，可通过优化带 lasso 惩罚的 D-trace 损失函数进行解码。为此，我们提出一种高效方法，能够输出精确解路径，优于以往仅在预选正则化参数上对解路径进行采样的方法。值得注意的是，所提方法计算复杂度低，尤其在差分网络稀疏时更为明显。我们在合成数据上的仿真表明，与现有方法相比，本策略在速度和精度上均具有优越性能。此外，在真实世界问题中，本策略在融合多源数据集以推断差分网络方面表现出很高的有效性，这一点得到了我们在肿瘤癌症耐药性实验结果的支撑。在后一情形中，本策略输出了与耐药性相关的重要基因，这些基因已被多项独立研究所证实。
英文摘要：
This paper addresses learning of sparse structural changes or differential network between two classes of non-paranormal graphical models. We assume a multi-source and heterogeneous dataset is available for each class, where the covariance matrices are identical for all non-paranormal graphical models. The differential network, which are encoded by the difference precision matrix, can then be decoded by optimizing a lasso penalized D-trace loss function. To this aim, an efficient approach is proposed that outputs the exact solution path, outperforming the previous methods that only sample from the solution path in pre-selected regularization parameters. Notably, our proposed method has low computational complexity, especially when the differential network are sparse. Our simulations on synthetic data demonstrate a superior performance for our strategy in terms of speed and accuracy compared to an existing method. Moreover, our strategy in combining datasets from multiple sources is shown to be very effective in inferring differential network in real-world problems. This is backed by our experimental results on drug resistance in tumor cancers. In the latter case, our strategy outputs important genes for drug resistance which are already confirmed by various independent studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
