计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
1期
137-142
,共6页
多元时间序列%特征降维%共同核主成分%角度优化%噪声%云计算平台
多元時間序列%特徵降維%共同覈主成分%角度優化%譟聲%雲計算平檯
다원시간서렬%특정강유%공동핵주성분%각도우화%조성%운계산평태
Multivariate Time Series(MTS)%feature dimension reduction%common kernel principal component%angle optimized%noise%cloud computing platform
多元时间序列具有高噪声、非线性和海量的特点，但传统基于距离的降维方法难以有效的应对噪声带来的子空间偏移和数据的爆炸式增长。在基于角度优化的全局嵌入算法和共同核主成分分析方法的基础上，提出了一种基于角度优化的共同核主成分分析方法，并将该方法依托Hadoop平台进行了并行化改进，有效解决了噪音带来的子空间偏移和海量数据带来的巨大运算量问题。通过实验，对算法的有效性、运行效率及伸缩性进行了验证，结果表明提出的方法可以有效地对含有噪声的多元时间序列进行降维；基于Hadoop平台并行后的方法具有良好的运行效率和伸缩性。
多元時間序列具有高譟聲、非線性和海量的特點，但傳統基于距離的降維方法難以有效的應對譟聲帶來的子空間偏移和數據的爆炸式增長。在基于角度優化的全跼嵌入算法和共同覈主成分分析方法的基礎上，提齣瞭一種基于角度優化的共同覈主成分分析方法，併將該方法依託Hadoop平檯進行瞭併行化改進，有效解決瞭譟音帶來的子空間偏移和海量數據帶來的巨大運算量問題。通過實驗，對算法的有效性、運行效率及伸縮性進行瞭驗證，結果錶明提齣的方法可以有效地對含有譟聲的多元時間序列進行降維；基于Hadoop平檯併行後的方法具有良好的運行效率和伸縮性。
다원시간서렬구유고조성、비선성화해량적특점，단전통기우거리적강유방법난이유효적응대조성대래적자공간편이화수거적폭작식증장。재기우각도우화적전국감입산법화공동핵주성분분석방법적기출상，제출료일충기우각도우화적공동핵주성분분석방법，병장해방법의탁Hadoop평태진행료병행화개진，유효해결료조음대래적자공간편이화해량수거대래적거대운산량문제。통과실험，대산법적유효성、운행효솔급신축성진행료험증，결과표명제출적방법가이유효지대함유조성적다원시간서렬진행강유；기우Hadoop평태병행후적방법구유량호적운행효솔화신축성。
Multivariate Time Series(MTS)is featured as high noises, nonlinear and mess. However, the traditional method based on distance to reduce dimension has difficulty in dealing with the subspace deviation which is caused by noises and the dramatic increase of data. In this essay, a new analysis is proposed based on Angle Optimized Global Embedding (AOGE)and Principal Component Analysis(PCA). This new analysis method is equipped on Hadoop platform for improved parallelization which effectively deals with the subspace deviation caused by noises and the calculation problem caused by massive data. Through experiment, the new method has proved its effectiveness, operating efficiency and flexibility, showing that this method can effectively reduce dimension of MTS with noises. The parallelized method which is bases on the Hadoop platform has good efficiency and flexibility.
