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通过乔列斯基分解(Cholesky Decomposition)提升在SPD流形上的图神经网络 ...
提出基于对称正定矩阵流形(SPD)的图神经网络模型SPDGNN,利用Log-Cholesky度量直接在SPD流形上操作,避免频繁映射提升效率,开发专用邻居聚合和非线性模块,构建多类逻辑回归框架,实验证明在层次结构数据分类任务中优于传统欧几里得和超曲面GNN。 非欧几里 ...
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