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Dimension Reduction with Locally Adjusted Graphs
Yingfan Wang, Yiyang Sun, Haiyang Huang +1
Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptom…
Navigating the Effect of Parametrization for Dimensionality Reduction
Haiyang Huang, Yingfan Wang, Cynthia Rudin
Parametric dimensionality reduction methods have gained prominence for their ability to generalize to unseen datasets, an advantage that traditional approaches typically lack. Desp…
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Yingfan Wang, Haiyang Huang, Cynthia Rudin +1
Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMAP have demonstrated impressive visualization performance on many real world datasets. One tension that has always…