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Information on published tensor decomposition algorithms.

NIAID Data Ecosystem2026-05-02 收录

张量分解算法

算法资源整理

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https://figshare.com/articles/dataset/Information_on_published_tensor_decomposition_algorithms_/27946783 数据链接链接失效反馈

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Information on published tensor decomposition algorithms.

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创建时间：

2024-12-02

张量分解算法

性能优化

The static method CTD-S outperforms the state of-the-art Tensor-CUR in terms of time, memory usage, and accuracy. The dynamic method CTD-D is the fastest.

NIAID Data Ecosystem60

Comparison of time complexity (per iteration) and memory usage of our proposed S3CMTF and other CMTF algorithms.

张量分解算法

复杂度分析

S3CMTF-opt shows the lowest time complexity and S3CMTF-base shows the lowest memory usage. For simplicity, we assume that all modes are of size I, of rank J, and an I × K matrix is coupled to one mode

Figshare2019-06-28 更新50

HOSVD-ALS

张量分解算法

计算流体力学

Introduction The truncated Tucker decomposition, also known as the truncated higher-order singular value decomposition (HOSVD), has been extensively utilized as an efficient tool in many applica

Figshare2020-04-09 更新30

Bayesian Adaptive Tucker Decompositions for Tensor Factorization

张量分解算法

化学计量学建模

Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibility brings the challenge of selecting the appropria

DataCite Commons2025-06-01 更新60

Comparison of our proposed CTD and the existing Tensor-CUR.

张量分解算法

高维数据近似计算

The static method CTD-S outperforms the state of-the-art Tensor-CUR in terms of time, memory usage, and accuracy. The dynamic method CTD-D is the fastest.

Figshare2018-07-25 更新40

Information on published tensor decomposition algorithms.

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