Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
这篇论文表明，维度降低方法UMAP和t-SNE可以近似地重新表述为概率分布模型引入的广义Wishart模型的MAP推理方法。这种解释为这些算法提供了更深刻的理论洞察，同时为类似维度降低方法的研究提供了工具。
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.
https://arxiv.org/abs/2405.17412
https://arxiv.org/pdf/2405.17412.pdf