CAJ | 학술논문

混合鉴别分析(hybrid discirminant analysis,HDA)融合了主元分析和线性鉴别分析的优点,适合更多的数据分布,在实际应用中取得了较好的效果.然而HDA不适合复杂、非线性数据结构的维数约简.首先通过特征映射把数据样本映射到高维线性空间,然后建立线性 HDA 模型,基于流形学习理论和 LSSVM(least square support vector machine)框架,给出了保持数据局部结构的核 HDA(locality preserving kernel HDA, LPKHDA)算法.提出了基于散度矩阵的诱导核空间选择方法,通过把模型参数选择问题转化为最优诱导核空间选择问题来求取最优模型参数,通过梯度下降法求取核函数参数和散度矩阵系数最优值.基于Adaboost实现了LPKHDA算法.在UCI数据和人脸图像上进行仿真实验,结果表明与HDA算法相比,新算法不仅较好地解决了模型参数选择问题,且具有较好的性能.
혼합감별분석(hybrid discirminant analysis,HDA)융합료주원분석화선성감별분석적우점,괄합경다적수거분포,재실제응용중취득료교호적효과.연이HDA불괄합복잡、비선성수거결구적유수약간.수선통과특정영사파수거양본영사도고유선성공간,연후건립선성 HDA 모형,기우류형학습이론화 LSSVM(least square support vector machine)광가,급출료보지수거국부결구적핵 HDA(locality preserving kernel HDA, LPKHDA)산법.제출료기우산도구진적유도핵공간선택방법,통과파모형삼수선택문제전화위최우유도핵공간선택문제래구취최우모형삼수,통과제도하강법구취핵함수삼수화산도구진계수최우치.기우Adaboost실현료LPKHDA산법.재UCI수거화인검도상상진행방진실험,결과표명여HDA산법상비,신산법불부교호지해결료모형삼수선택문제,차구유교호적성능.
Hybrid discriminant analysis (HDA) which combines principal component analysis (PCA) with linear discriminant analysis (LDA) can achieve satisfying performance for data set following complex distribution. However, HDA can not work well for complex and nonlinear distributed data. Based on manifold learning and LSSVM (least square support vector machine), this paper proposes a kernel-induced space selection-based local preserving hybrid discriminant analysis (LPKHDA) algorithm to overcome these drawbacks. In this algorithm, the input data are firstly mapped into high dimensional feature space through nonlinear map and linear HDA is modeled in the feature space. This paper discusses a kernel-induced space selection approach based on divergence matrix, which transforms LPKHDA model selection to kernel-induced space selection for optimal model parameter, and uses gradient descent method to achieve kernel parameter and optimal divergence matrix coefficient. Based on Adaboost, LPKHDA algorithm (Boosted LPKHDA ) is implemented. Several applications and experiments on UCI and face data set show that the algorithm can effectively deal with the problems of the existing HDA algorithms and provide good performance.