Pages that link to "Item:Q4816852"

The following pages link to Laplacian Eigenmaps for Dimensionality Reduction and Data Representation (Q4816852):

Displaying 50 items.

• A distributed algorithm for the cluster-based outlier detection using unsupervised extreme learning machines (Q1992535) (← links)
• A manifold-based approach to sparse global constraint satisfaction problems (Q2010101) (← links)
• Stability analysis of learning algorithms for ontology similarity computation (Q2016684) (← links)
• Finding robust transfer features for unsupervised domain adaptation (Q2019700) (← links)
• Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold (Q2020284) (← links)
• Model-free data-driven computational mechanics enhanced by tensor voting (Q2020810) (← links)
• Nonlocal-interaction equation on graphs: gradient flow structure and continuum limit (Q2021745) (← links)
• Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
• Adapted single-cell consensus clustering (adaSC3) (Q2022498) (← links)
• Rank-constrained nonnegative matrix factorization for data representation (Q2023226) (← links)
• Data-driven Koopman operator approach for computational neuroscience (Q2023876) (← links)
• A kernel method for learning constitutive relation in data-driven computational elasticity (Q2024599) (← links)
• Diffusion representation for asymmetric kernels (Q2029166) (← links)
• Riemannian gradient descent methods for graph-regularized matrix completion (Q2029849) (← links)
• A novel discriminant locality preserving projections method (Q2036195) (← links)
• Reproducing kernel Hilbert space compactification of unitary evolution groups (Q2036491) (← links)
• Structure-guided attributed network embedding with ``centroid'' enhancement (Q2047521) (← links)
• Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
• Some extensions of E. Stein's work on Littlewood-Paley theory applied to symmetric diffusion semigroups (Q2050522) (← links)
• Dimensionality reduction based on kCCC and manifold learning (Q2051142) (← links)
• A Riemannian geometric framework for manifold learning of non-Euclidean data (Q2051579) (← links)
• Optimality of spectral clustering in the Gaussian mixture model (Q2054516) (← links)
• An explicit characterization of the domain of the infinitesimal generator of a symmetric diffusion semigroup on \(m_p\) of a complete positive sigma-finite measure space (Q2054575) (← links)
• A direct approach for function approximation on data defined manifolds (Q2057766) (← links)
• Distance preserving model order reduction of graph-Laplacians and cluster analysis (Q2059823) (← links)
• A topological approach to spectral clustering (Q2069945) (← links)
• Heat kernel analysis of syntactic structures (Q2071526) (← links)
• Generalized penalty for circular coordinate representation (Q2072668) (← links)
• Chaoticity versus stochasticity in financial markets: are daily S\&P 500 return dynamics chaotic? (Q2076249) (← links)
• Neurons on amoebae (Q2100037) (← links)
• Quantum algorithm for Laplacian eigenmap via Rayleigh quotient iteration (Q2102192) (← links)
• Semi-parametric Bayes regression with network-valued covariates (Q2102416) (← links)
• Data-driven efficient solvers for Langevin dynamics on manifold in high dimensions (Q2105115) (← links)
• Harmonic analysis on directed graphs and applications: from Fourier analysis to wavelets (Q2105123) (← links)
• Graph-theoretic algorithms for Kolmogorov operators: approximating solutions and their gradients in elliptic and parabolic problems on manifolds (Q2111184) (← links)
• Simplicial degree in complex networks. Applications of topological data analysis to network science (Q2120699) (← links)
• Robust distribution-based nonnegative matrix factorizations for dimensionality reduction (Q2123493) (← links)
• Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques (Q2125604) (← links)
• Intrinsic dimension estimation based on local adjacency information (Q2127086) (← links)
• Graph coarsening: from scientific computing to machine learning (Q2128866) (← links)
• A review on spectral clustering and stochastic block models (Q2132025) (← links)
• Quantitative analysis of financial system fragility based on manifold curvature (Q2158963) (← links)
• Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems: a new approach using secants (Q2163754) (← links)
• Multiscale regression on unknown manifolds (Q2167604) (← links)
• Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation (Q2168682) (← links)
• Data-driven modeling with fuzzy sets and manifolds (Q2169224) (← links)
• Hierarchical analysis of Chinese financial market based on manifold structure (Q2171343) (← links)
• Multi-view kernel consensus for data analysis (Q2175021) (← links)
• On active learning methods for manifold data (Q2177723) (← links)
• Matrix exponential based discriminant locality preserving projections for feature extraction (Q2179100) (← links)