AI RESEARCH
Supervised Latent Restructuring for Small-Data Quantum Learning in Plant Phenomics
arXiv CS.LG
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ArXi:2605.20413v1 Announce Type: new High-dimensional biological data often exhibit a severe mismatch between feature dimensionality and sample size, making reliable classification difficult in extremely small-data regimes. In these settings, kernel methods can lose discriminative power when latent compression fails to preserve class-separating structure.