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Showing 1 to 6 of 6 for “"Kernel matrices"”.
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Convergence Rates of Spectral Distribution of Random Inner Product Kernel Matrices
… the first part, we focus on random inner product kernel matrices. Under various assumptions, many authors have proved that the limiting empirical spectral distribution (ESD) of such matrices A converges to the Marchenko- Pastur distribution. Here, we establish the corresponding rate of …
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Identification of parametric finite-element models using experimental modal data
… a set of scalar parameters multiplying element kernel matrices. The parameters of the finite-element model can represent stiffness and inertia properties, as well as axial forces if linearized buckling is assumed. Optimization of an orthogonality-based objective functional is employed to …
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Efficient Algorithms for Vector Similarities
… between vectors encoded in similarity matrices. Our algorithms compute on similarity matrices, such as distance or kernel matrices, without ever initializing them, thus avoiding an infeasible quadratic time bottleneck. Overall, the main message of this thesis is that sublinear …
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Multi-target Prediction Methods for Bioinformatics: Approaches for Protein Function Prediction and Candidate Discovery for Gene Regulatory Network Expansion
… examples is coded by the combination of multiple kernel matrices, while relations among target variables are expressed as logical constraints. Both, the mislabeling of examples and the infringement of logical rules are penalized by the loss function, but Ocelot do not forces hierarchical …
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Statistical methods for multi-omic data integration
… integration of multi-omic data, called kernel learning integrative clustering (KLIC). This approach is based on the idea to frame the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to …
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The proxy point method for rank-structured matrices
… to reduce computation and storage cost for dense matrices defined by interactions between many bodies. The main bottleneck for their application is the expensive computation required to represent a matrix in a rank-structured matrix format which involves compressing specific matrix blocks into …