Pages that link to "Item:Q5431015"

The following pages link to Nonlinear Dimensionality Reduction (Q5431015):

Displaying 50 items.

• A kernel-based method for data-driven Koopman spectral analysis (Q317185) (← links)
• Optimization of the maximum likelihood estimator for determining the intrinsic dimensionality of high-dimensional data (Q327034) (← links)
• Geometric data manipulation with Clifford algebras and Möbius transforms (Q331636) (← links)
• Integral manifolds for uncertain impulsive differential-difference equations with variable impulsive perturbations (Q339844) (← links)
• An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification (Q448318) (← links)
• A regularized graph layout framework for dynamic network visualization (Q468664) (← links)
• Challenges in data science: a complex systems perspective (Q528296) (← links)
• Curvature analysis of frequency modulated manifolds in dimensionality reduction (Q535354) (← links)
• Extending metric multidimensional scaling with Bregman divergences (Q632606) (← links)
• Dynamical criteria for the evolution of the stochastic dimensionality in flows with uncertainty (Q655563) (← links)
• Cohort-based kernel visualisation with scatter matrices (Q663371) (← links)
• Data-driven non-linear elasticity: constitutive manifold construction and problem discretization (Q683898) (← links)
• An effective discretization method for disposing high-dimensional data (Q726197) (← links)
• A sparse grid based method for generative dimensionality reduction of high-dimensional data (Q729476) (← links)
• Unsupervised interaction-preserving discretization of multivariate data (Q736501) (← links)
• The flood algorithm -- a multivariate, self-organizing-map-based, robust location and covariance estimator (Q746209) (← links)
• Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks (Q825483) (← links)
• Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions (Q888561) (← links)
• A data-driven approximation of the koopman operator: extending dynamic mode decomposition (Q897161) (← links)
• On some convergence properties of the subspace constrained mean shift (Q898066) (← links)
• Shape classification by manifold learning in multiple observation spaces (Q903623) (← links)
• Text data mining: theory and methods (Q975565) (← links)
• A manifold learning approach to data-driven computational elasticity and inelasticity (Q1639583) (← links)
• kPCA-based parametric solutions within the PGD framework (Q1639585) (← links)
• Evolutionary feature selection for big data classification: a MapReduce approach (Q1665073) (← links)
• Intrinsic dimension estimation: relevant techniques and a benchmark framework (Q1666523) (← links)
• A new class of multi-stable neural networks: stability analysis and learning process (Q1669075) (← links)
• Random walks and diffusion on networks (Q1687598) (← links)
• Bayesian optimization of empirical model with state-dependent stochastic forcing (Q1694078) (← links)
• A perturbative picture of cubic tensors in dually flat spaces (Q1742874) (← links)
• Kernel penalized K-means: a feature selection method based on kernel K-means (Q1750032) (← links)
• On the effect of phase transition on the manifold dimensionality: application to the Ising model (Q1785711) (← links)
• Learning algebraic varieties from samples (Q1790935) (← links)
• Novel high intrinsic dimensionality estimators (Q1945123) (← links)
• Nonparametric semi-supervised classification with application to signal detection in high energy physics (Q2082459) (← links)
• Learning non-Markovian physics from data (Q2128336) (← links)
• Geometry and generalization: eigenvalues as predictors of where a network will fail to generalize (Q2148963) (← links)
• Nonlinear mapping and distance geometry (Q2174887) (← links)
• A framework for data-driven structural analysis in general elasticity based on nonlinear optimization: the static case (Q2184306) (← links)
• A simple test for zero multiple correlation coefficient in high-dimensional normal data using random projection (Q2189584) (← links)
• Supervised distance preserving projection using alternating direction method of multipliers (Q2190310) (← links)
• Flexible semi-supervised embedding based on adaptive loss regression: application to image categorization (Q2195309) (← links)
• Approximation of functions over manifolds: a moving least-squares approach (Q2199793) (← links)
• Manifold approximation by moving least-squares projection (MMLS) (Q2216655) (← links)
• Computational complexity of learning algebraic varieties (Q2221768) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• Spectral dimensionality reduction for Bregman information (Q2279965) (← links)
• Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction (Q2281470) (← links)
• Sampling from manifold-restricted distributions using tangent bundle projections (Q2302511) (← links)
• A review and proposal of (fuzzy) clustering for nonlinearly separable data (Q2302802) (← links)