Nov 10, 2023 · We evaluate the approach on two benchmark datasets -- scurve and MNIST -- with strong and weak scaling using OpenMP and MPI on dense matrices with maximum size ...

We evaluate the approach on two benchmark datasets – scurve and MNIST – with strong and weak scaling using OpenMP and MPI on dense matrices with maximum size of

Efficient and Scalable Kernel Matrix Approximation using Hierarchical Decomposition. The dockerfile contains commands to install all the run-time ...

A global method, i.e. a method that requires all data points simultaneously, scales with the data dimension \(N\) and not with the intrinsic dimension \(d\); ...

An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a ...

Efficient and Scalable Kernel Matrix Approximations Using Hierarchical Decomposition. https://doi.org/10.1007/978-981-97-0065-3_1.

A hierarchical approximation approach scales roughly with a runtime complexity of $$\mathcal {O}(Nlog(N))$$ vs. $$\mathcal {O}(N^{3})$$ for a classic approach.

A hierarchical approximation approach scales roughly with a runtime complexity of O ( N l o g ( N ) ) vs. O ( N 3 ) for a classic approach. We evaluate the ...

Jun 9, 2022 · GOFMM is a library that provides hierarchical algorithms for matrix approximations and evaluations. For the eigendecompositions, implicitly ...

Efficient and Scalable Kernel Matrix Approximations using Hierarchical Decomposition. 階層的分解を用いた効率的でスケーラブルなカーネル行列近似【JST・京大機械 ...