A robust SCQM model extending SQMF to accommodate generalized Gaussian and radial Laplace noise, solved via gradient descent with line search and validated through sensitivity analysis and experiments.
Locally defined principal curves and surfaces.Journal of Machine learning research, 12(Apr):1249– 1286
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning
A robust SCQM model extending SQMF to accommodate generalized Gaussian and radial Laplace noise, solved via gradient descent with line search and validated through sensitivity analysis and experiments.