Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
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. We formulate the problem as the joint recovery of the low-rank factors, loadings, and the nonlinear link function from possibly incomplete and noisy observations and propose a projected block coordinate descent (BCD) algorithm with explicit regularization to address scale and rotational ambi...
| Authors | Yutong Chao, Resat Gökhan, Jalal Etesami et al. |
|---|---|
| Primary category | stat.ML |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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