.png){kind=icon}
返回
Fast-mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High-Dimensional Big Data
.png){kind=icon}
OA
AI
DOI：10.1002/int.21833.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the advent of large-scale problems, feature selection has become a fundamental preprocessing step to reduce input dimensionality. The minimum-redundancy-maximum-relevance (mRMR) selector is considered one of the most relevant methods for dimensionality reduction due to its high accuracy. However, it is a computationally expensive technique, sharply affected by the number of features. This paper presents fast-mRMR, an extension of mRMR, which tries to overcome this computational burden. Associated with fast-mRMR, we include a package with three implementations of this algorithm in several platforms, namely, CPU for sequential execution, GPU (graphics processing units) for parallel computing, and Apache Spark for distributed computing using big data technologies. (C) 2016 Wiley Periodicals, Inc.
Keyword:
FEATURE-SELECTION
MUTUAL INFORMATION
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:
3.7
论文数:
3.1K
被引数:
8.1K
机构
引用论文
.png){kind=icon}
Simultaneously spatially and temporally focused femtosecond vortex beams for laser micromachiningOAAI
OAAI分享收藏分享收藏
查看更多内容