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- Low_Rank_Matrix_Approximations abstract "Low Rank Matrix Approximations are essential tools in the application of kernel methods to large-scale learning problems.Kernel methods (for instance, support vector machines or gaussian processes) project data points into a high-dimensional or infinite-dimensional feature space and find the optimal splitting hyperplane. In the kernel method the data is represented in a kernel matrix (or, Gram matrix). Lots of algorithms can solve machine learning problems using the kernel matrix. The main problem of kernel method is its high computational cost associated with kernel matrices. The cost is at least quadratic in the number of training data points, but most kernel methods include computation of matrix inversion or eigenvalue decomposition and the cost becomes cubic in the number of training data. Large training sets cause large storage and computational costs. In spite of low rank decomposition methods (Cholesky decomposition) reduce this cost, they continue to require computing the kernel matrix. One of the approaches to deal with this problem is Low Rank Matrix Approximations. The most popular examples of them are Nyström method and the Random Features. Both of them have been successfully applied to efficient kernel learning.".
- Low_Rank_Matrix_Approximations wikiPageExternalLink kernel-approximations-for-efficient.html.
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- Low_Rank_Matrix_Approximations wikiPageRevisionID "697844075".
- Low_Rank_Matrix_Approximations wikiPageWikiLink Algorithmic_efficiency.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Category:Kernel_methods_for_machine_learning.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Cholesky_decomposition.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Diagonal_matrix.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Eigendecomposition_of_a_matrix.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Feature_map.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Feature_vector.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Fourier_transform.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Gaussian_process.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Gramian_matrix.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Invertible_matrix.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Kernel_method.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Machine_learning.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Matrix_multiplication.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Monte_Carlo_method.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Nyström_method.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Orthogonal_matrix.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Proximity_(film).
- Low_Rank_Matrix_Approximations wikiPageWikiLink Radial_basis_function_kernel.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Random_variable.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Rank_(linear_algebra).
- Low_Rank_Matrix_Approximations wikiPageWikiLink Regularized_least_squares.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Singular_value.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Singular_value_decomposition.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Support_vector_machine.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Taxicab_geometry.
- Low_Rank_Matrix_Approximations wikiPageWikiLink Woodbury_matrix_identity.
- Low_Rank_Matrix_Approximations wikiPageUsesTemplate Template:Orphan.
- Low_Rank_Matrix_Approximations wikiPageUsesTemplate Template:Reflist.
- Low_Rank_Matrix_Approximations subject Category:Kernel_methods_for_machine_learning.
- Low_Rank_Matrix_Approximations hypernym Tools.
- Low_Rank_Matrix_Approximations type Software.
- Low_Rank_Matrix_Approximations comment "Low Rank Matrix Approximations are essential tools in the application of kernel methods to large-scale learning problems.Kernel methods (for instance, support vector machines or gaussian processes) project data points into a high-dimensional or infinite-dimensional feature space and find the optimal splitting hyperplane. In the kernel method the data is represented in a kernel matrix (or, Gram matrix). Lots of algorithms can solve machine learning problems using the kernel matrix.".
- Low_Rank_Matrix_Approximations label "Low Rank Matrix Approximations".
- Low_Rank_Matrix_Approximations wasDerivedFrom Low_Rank_Matrix_Approximations?oldid=697844075.
- Low_Rank_Matrix_Approximations isPrimaryTopicOf Low_Rank_Matrix_Approximations.