Pages that link to "Item:Q4816852"

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

Displaying 50 items.

• A continuous linear optimal transport approach for pattern analysis in image datasets (Q1669731) (← links)
• Image clustering based on sparse patch alignment framework (Q1676942) (← links)
• Graph regularized multiset canonical correlations with applications to joint feature extraction (Q1677005) (← links)
• Large-scale eigenvector approximation via Hilbert space embedding Nyström (Q1677860) (← links)
• Greedy approaches to semi-supervised subspace learning (Q1678697) (← links)
• Lithium-ion battery capacity estimation: a method based on visual cognition (Q1693799) (← links)
• Graph-based predictable feature analysis (Q1698840) (← links)
• Multiple penalized principal curves: analysis and computation (Q1703949) (← links)
• Neighborhood preserving convex nonnegative matrix factorization (Q1717782) (← links)
• Adaptive initialization method based on spatial local information for \(k\)-means algorithm (Q1719093) (← links)
• Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer (Q1727851) (← links)
• A natural diffusion distance and equivalence of local convergence and local equicontinuity for a general symmetric diffusion semigroup (Q1728532) (← links)
• SICA: subjectively interesting component analysis (Q1741396) (← links)
• Learning the geometry of common latent variables using alternating-diffusion (Q1742815) (← links)
• A unified framework for harmonic analysis of functions on directed graphs and changing data (Q1742819) (← links)
• Parsimonious representation of nonlinear dynamical systems through manifold learning: a chemotaxis case study (Q1742825) (← links)
• Bigeometric organization of deep nets (Q1742828) (← links)
• Iterated diffusion maps for feature identification (Q1748254) (← links)
• Adaptive graph construction using data self-representativeness for pattern classification (Q1750336) (← links)
• Intrinsic dimension estimation: advances and open problems (Q1750498) (← links)
• Graph-induced restricted Boltzmann machines for document modeling (Q1750503) (← links)
• Pairwise constraints based multiview features fusion for scene classification (Q1760419) (← links)
• Frame potential minimization for clustering short time series (Q1761310) (← links)
• Towards collaborative feature extraction for face recognition (Q1761733) (← links)
• Sparsity preserving discriminant projections with applications to face recognition (Q1793275) (← links)
• A multikernel-like learning algorithm based on data probability distribution (Q1793375) (← links)
• Nonlinear alignment and its local linear iterative solution (Q1793507) (← links)
• Robust structure preserving nonnegative matrix factorization for dimensionality reduction (Q1793532) (← links)
• The chaotic attractor analysis of DJIA based on manifold embedding and Laplacian eigenmaps (Q1793624) (← links)
• Spectral nonlinearly embedded clustering algorithm (Q1793769) (← links)
• Global similarity preserving hashing (Q1797823) (← links)
• ROML: a robust feature correspondence approach for matching objects in a set of images (Q1800046) (← links)
• Limit theorems for eigenvectors of the normalized Laplacian for random graphs (Q1800805) (← links)
• Embeddings of Riemannian manifolds with finite eigenvector fields of connection Laplacian (Q1800869) (← links)
• Locality pursuit embedding (Q1886657) (← links)
• Coupled action recognition and pose estimation from multiple views (Q1931616) (← links)
• Tensorized feature extraction technique for multimodality preserving manifold visualization (Q1932987) (← links)
• Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition (Q1942726) (← links)
• Random walk distances in data clustering and applications (Q1947049) (← links)
• Primal and dual model representations in kernel-based learning (Q1950324) (← links)
• Selection of variables and dimension reduction in high-dimensional non-parametric regression (Q1951796) (← links)
• Spectral clustering based on local linear approximations (Q1952238) (← links)
• A general learning framework using local and global regularization (Q1957872) (← links)
• Local Procrustes for manifold embedding: a measure of embedding quality and embedding algorithms (Q1959518) (← links)
• Semi-supervised local Fisher discriminant analysis for dimensionality reduction (Q1959540) (← links)
• Learning to rank on graphs (Q1959630) (← links)
• Regularized least squares locality preserving projections with applications to image recognition (Q1982448) (← links)
• Manifold learning with arbitrary norms (Q1982603) (← links)
• An extended shift-invert residual Arnoldi method (Q1983898) (← links)
• Think globally, fit locally under the manifold setup: asymptotic analysis of locally linear embedding (Q1990602) (← links)