A Data Reduction Algorithm Based on K-L Feature Compression for Cloud Computing

NIE Jun

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A Data Reduction Algorithm Based on K-L Feature Compression for Cloud Computing

更新时间：2026-09-04

- A Data Reduction Algorithm Based on K-L Feature Compression for Cloud Computing
- Vol. 33, Issue 2, Pages: 127-131(2016)
- 作者机构：
  
  广东科技学院机构计算机系,广东,东莞,523083
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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聂军. 基于K-L特征压缩的云计算冗余数据降维算法[J]. 微电子学与计算机, 2016, 33(2): 127-131.

NIE Jun. A Data Reduction Algorithm Based on K-L Feature Compression for Cloud Computing[J]. 2016, 33(2): 127-131.
聂军. 基于K-L特征压缩的云计算冗余数据降维算法[J]. 微电子学与计算机, 2016, 33(2): 127-131. DOI：

NIE Jun. A Data Reduction Algorithm Based on K-L Feature Compression for Cloud Computing[J]. 2016, 33(2): 127-131. DOI：

摘要

提出一种基于K-L(Karhunen-Loeve Transform)特征压缩的云计算冗余数据降维算法.在冗余数据的重构相空间中进行高维特征提取

采用K-L特征压缩方法降低云计算冗余数据的维数

设计改进的FIR滤波算法实现冗余数据滤除.仿真结果表明

采用该算法能有效实现对云计算冗余数据的特征空间降维和滤除处理

提高云计算读写速度

降低计算开销.

Abstract

A K-L (Karhunen-Loeve Transform) based algorithm for reducing the dimension of redundant data in the cloud computing is proposed. High dimensional feature extraction is performed in the reconstructed phase space of redundant data

and K-L feature compression method is adopted to reduce the dimension of redundant data in the cloud computing

and the improved FIR filtering algorithm is designed to realize the redundancy data filtering. Simulation results show that the proposed algorithm can effectively achieve the feature space dimension reduction of the redundant data in the cloud computing

and can improve the speed of cloud computing

and reduce the computational cost.

关键词

Keywords

references

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