计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2014年
7期
2412-2416,2493
,共6页
高斯过程隐变量模型%谱算法%多核迭代%核函数%分类器%支持向量机
高斯過程隱變量模型%譜算法%多覈迭代%覈函數%分類器%支持嚮量機
고사과정은변량모형%보산법%다핵질대%핵함수%분류기%지지향량궤
Gaussian process latent variable mode%spectrum algorithm%multi-kernel iteration%kernel function%data classifier%support vector machine
针对传统谱算法在数据分类问题中的局限,提出一种基于共有GP-LVM和改进型SVM的数据分类算法。通过高斯过程(GP)对数据流形建立概率模型,得到高斯过程隐变量模型(GP-LVM),分析GP-LVM得到数据流形的特征信息;利用多核迭代的方式,改进SVM算法中的核函数,建立最佳的数据分类器,实现数据分类。选取FERET、UCI多类数据库进行对比实验,实验结果表明,该算法可以有效地对高维数据进行分类,针对均衡数据和不均衡数据也具有良好的分类效果,较传统算法在分类准确率上提高8%左右。
針對傳統譜算法在數據分類問題中的跼限,提齣一種基于共有GP-LVM和改進型SVM的數據分類算法。通過高斯過程(GP)對數據流形建立概率模型,得到高斯過程隱變量模型(GP-LVM),分析GP-LVM得到數據流形的特徵信息;利用多覈迭代的方式,改進SVM算法中的覈函數,建立最佳的數據分類器,實現數據分類。選取FERET、UCI多類數據庫進行對比實驗,實驗結果錶明,該算法可以有效地對高維數據進行分類,針對均衡數據和不均衡數據也具有良好的分類效果,較傳統算法在分類準確率上提高8%左右。
침대전통보산법재수거분류문제중적국한,제출일충기우공유GP-LVM화개진형SVM적수거분류산법。통과고사과정(GP)대수거류형건립개솔모형,득도고사과정은변량모형(GP-LVM),분석GP-LVM득도수거류형적특정신식;이용다핵질대적방식,개진SVM산법중적핵함수,건립최가적수거분류기,실현수거분류。선취FERET、UCI다류수거고진행대비실험,실험결과표명,해산법가이유효지대고유수거진행분류,침대균형수거화불균형수거야구유량호적분류효과,교전통산법재분류준학솔상제고8%좌우。
The traditional spectrum algorithms had been limited in data classification problem.For its characteristics of problem, a novel algorithm based on common Gaussian process latent variable mode (CGP-LVM)and modified support vector machine (SVM)was proposed.Firstly,the probabilistic model of data manifold was established by the Gaussian process (GP),and GP-LVM was gotten.The feature information was gotten by analyzing the data manifold.Secondly,the kernel function of SVM was modified by the multi-kernel iteration.Thereafter,the optimal data classifier was established.Finally,the data classification was achieved.Some data sets were selected as the experimental data,which consisted of FERET and UCI.A lot of experiments had been done.The results showed that the proposed method had not only a great effect on accomplishing high-dimension data classi-fication,but a great classification effect for the balanced data and the imbalanced data.The classification accuracy rate was higher than the traditional algorithms by 8%.