This paper shows that the dimensionality reduction methods, UMAP and t-SNE, can be approximately recast as MAP inference methods corresponding to a generalized Wishart-based model introduced in ProbDR. This interpretation offers deeper theoretical insights into these algorithms, while introducing tools with which similar dimensionality reduction methods can be studied.

翻译：本文证明，降维方法UMAP与t-SNE可近似重构为对应于ProbDR中引入的广义Wishart模型的MAP推断方法。这一阐释为理解此类算法提供了更深入的理论视角，同时为研究同类降维方法引入了新的分析工具。