dimension selection 1network embedding 1random matrix theory 1spectral graph theory 1stochastic block models 1
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math.ST2026
Network Signflip Parallel Analysis for Selecting the Embedding Dimension
David Hong, Joshua Cape
The paper proposes a data‑driven spectral technique called NetFlipPA that determines how many dimensions to keep when embedding large heterogeneous networks by comparing eigenvalue…
math.ST2026
Selecting the number of components in PCA via random signflips
David Hong, Yue Sheng, Edgar Dobriban
Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selec…