We investigate the properties of a class of regularisation-free approaches for Gaussian graphical inference based on the information-geometry-driven sequential growth of initially edgeless graphs. Relating the growth of a graph to a coordinate descent process, we characterise the fully-corrective descents corresponding to information-optimal growths, and propose numerically efficient strategies for their approximation. We demonstrate the ability of the proposed procedures to reliably extract sparse graphical models while limiting the number of false detections, and illustrate how activation ranks can provide insight into the informational relevance of edge sets. The considered approaches are tuning-parameter-free and have complexities akin to coordinate descents.

翻译：本文研究了一类基于信息几何驱动的无正则化高斯图推断方法，该方法从初始无边的图开始进行序列增长。通过将图的增长过程与坐标下降过程相关联，我们刻画了对应于信息最优增长的完全校正下降，并提出了数值高效的近似策略。我们证明了所提方法能够可靠地提取稀疏图模型，同时限制误检数量，并阐释了激活排序如何揭示边集的信息相关性。所考虑的方法无需调参，且计算复杂度与坐标下降算法相当。