AI RESEARCH
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
arXiv CS.LG
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ArXi:2605.26271v1 Announce Type: cross We study a nonlinear factor model in which observed responses depend on low-rank latent factors through an unknown monotone link function. This setting is challenging and largely underexplored due to severe nonconvexity and identifiability issues. The link function is assumed to lie in a reproducing kernel Hilbert space (RKHS), enabling flexible nonparametric modeling while preserving identifiability.