.png){kind=icon}
返回
Nonnegative Tensor Factorization Accelerated Using GPGPU
.png){kind=icon}
PRE
AI
DOI：10.1109/TPDS.2010.194.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article presents an optimized algorithm for Nonnegative Tensor Factorization (NTF), implemented in the CUDA (Compute Uniform Device Architecture) framework, that runs on contemporary graphics processors and exploits their massive parallelism. The NTF implementation is primarily targeted for analysis of high-dimensional spectral images, including dimensionality reduction, feature extraction, and other tasks related to spectral imaging; however, the algorithm and its implementation are not limited to spectral imaging. The speedups measured on real spectral images are around 60 - 100x compared to a traditional C implementation compiled with an optimizing compiler. Since common problems in the field of spectral imaging may take hours on a state-of-the-art CPU, the speedup achieved using a graphics card is attractive. The implementation is publicly available in the form of a dynamically linked library, including an interface to MATLAB, and thus may be of help to researchers and engineers using NTF on large problems.
Keyword:
Nonnegative tensor factorization
spectral analysis
GPU
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
5.2K
被引数:
1.1W
机构
引用论文
New polymorphic phase of arachidic acid crystal: structure, intermolecular interactions, low-temperature stability and Raman spectroscopy combined with DFT calculations.png)
RSC Advances
OAAI
RSC AdvancesIF0
OAAI分享收藏Physiological Arousal of Beginning Counselors In Relation To Disabled And Non-Disabled Clients与残疾和非残疾客户有关的初级辅导员的生理唤醒
PREAI
分享收藏
没有更多内容
没有更多内容