95 citations · 217 across the 25 of their papers we have counts for
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Principal Component Analysis in Space Forms
Puoya Tabaghi, Michael Khanzadeh, Yusu Wang +1
Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical a…
Unperturbed: spectral analysis beyond Davis-Kahan
Justin Eldridge, Mikhail Belkin, Yusu Wang
Classical matrix perturbation results, such as Weyl's theorem for eigenvalues and the Davis-Kahan theorem for eigenvectors, are general purpose. These classical bounds are tight in…
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering
Justin Eldridge, Mikhail Belkin, Yusu Wang
Hierarchical clustering is a popular method for analyzing data which associates a tree to a dataset. Hartigan consistency has been used extensively as a framework to analyze such c…