论文标题
等距高斯流程的潜在变量模型的差异数据
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
论文作者
论文摘要
我们提出了一个概率模型，其中潜在变量尊重建模数据的距离和拓扑。该模型利用生成的歧管的Riemannian几何形状赋予潜在空间以明确定义的随机距离度量，该距离局部建模为Nakagami分布。这些随机距离试图通过检查过程与沿邻域图的距离尽可能相似。基于成对距离的观察结果，通过变异推断推断该模型。我们演示了新模型如何在学习的歧管中编码不变。
We present a probabilistic model where the latent variable respects both the distances and the topology of the modeled data. The model leverages the Riemannian geometry of the generated manifold to endow the latent space with a well-defined stochastic distance measure, which is modeled locally as Nakagami distributions. These stochastic distances are sought to be as similar as possible to observed distances along a neighborhood graph through a censoring process. The model is inferred by variational inference based on observations of pairwise distances. We demonstrate how the new model can encode invariances in the learned manifolds.
