Manifold Learning Theory and ApplicationsYunqian Ma, Yun Fu CRC Press, 20 thg 12, 2011 - 314 trang Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread |
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Robust Laplacian Eigenmaps Using Global Information | |
Density Preserving Maps | |
Bibliography | |
Sample Complexity in Manifold Learning | |
Manifold Alignment | |
Large Scale Manifold Learning | |
Metric and Heat Kernel | |
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Thuật ngữ và cụm từ thông dụng
3-manifold algorithm analysis approach approximation body configuration bound circle packing clustering Color Insert Column sampling compute conformal geometry conformal mapping constraints coordinates correspondence information curvature flow curves d-dimensional data points datasets defined denote density estimation diffeomorphism differential dimension discrete domain eigenvalues eigenvectors embedding space equation Euclidean example framework geodesic distance geometry given global heat kernel high dimensional hyperbolic IEEE input space interpolation intrinsic Isomap joint Laplacian Laplace–Beltrami operator Laplacian Eigenmaps latent space Lemma Locally Linear Embedding loss function low-dimensional manifold alignment manifold learning matching matrix mesh multiple neighborhood graph neighbors nonlinear dimensionality reduction Nyström method optimal orthogonal parameterization principal curves problem protein random representation represented Ricci flow Riemann surface Riemannian manifold Riemannian metric sample complexity Section shape shown in Figure shows similarity singular values singular vectors spatial spectral structure style factors submanifold subspace tetrahedron Theorem topological triangle truncated tetrahedron visual