AI RESEARCH
Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding
arXiv CS.LG
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ArXi:2604.24692v1 Announce Type: new We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without greedy search. NBSE constructs a sparse similarity graph on the samples and identifies the Nishimori temperature $\beta_N$ the critical inverse temperature at which the Bethe Hessian becomes singular. The corresponding smallest eigenvector captures the dominant mode of an intrinsically degree-corrected diffusion process, naturally reweighting nodes to prevent hub dominance.