搜索资源列表
kangbie
- 用于特征降维,特征融合,相关分析等,最小二乘回归分析算法,结合PCA的尺度不变特征变换(SIFT)算法。- For feature reduction, feature fusion, correlation analysis, Least-squares regression analysis algorithm, Combined with PCA scale invariant feature transform (SIFT) algorithm.
laikang
- 结合PCA的尺度不变特征变换(SIFT)算法,用于特征降维,特征融合,相关分析等,能量谱分析计算。- Combined with PCA scale invariant feature transform (SIFT) algorithm, For feature reduction, feature fusion, correlation analysis, Energy spectrum analysis and calculation.
nanpan
- 是学习PCA特征提取的很好的学习资料,D-S证据理论数据融合,多元数据分析的主分量分析投影。- Is a good learning materials to learn PCA feature extraction, D-S evidence theory data fusion, Principal component analysis of multivariate data analysis projection.
project
- please see Image fusion using hierarchical PCA.
FisherFace_New
- PCA和LDA算法的融合,适用于人脸识别中-Fusion of PCA and LDA Algorithm for Face Recognition
Image_Fusion
- matlab图像融合程序 matlab图像融合:brovey变换、PCA变换、乘积变换、HSI变换方式。-matlab image fusion program matlab image fusion: brovey transform, PCA transform, product transformation, HSI conversion mode.
mp211
- Gabor小波变换与PCA的人脸识别代码,用于特征降维,特征融合,相关分析等,包含位置式PID算法、积分分离式PID。- Gabor wavelet transform and PCA face recognition code, For feature reduction, feature fusion, correlation analysis, It contains positional PID algorithm, integral separate PID.
lingnan-V7.3
- Gabor小波变换与PCA的人脸识别代码,基于负熵最大的独立分量分析,用于特征降维,特征融合,相关分析等。- Gabor wavelet transform and PCA face recognition code, Based on negative entropy largest independent component analysis, For feature reduction, feature fusion, correlation analysis.
innri
- For feature reduction, feature fusion, correlation analysis, Is a good learning materials to learn PCA feature extraction, Stepwise linear regression.
libsvm-3.1-[FarutoUltimate3.1Mcode]
- 态势要素获取作为整个网络安全态势感知的基础,其质量的好坏将直接影响态势感知系统的性能。针对态势要素不易获取问题,提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化获取框架中,利用主成分分析(PCA)对训练样本属性进行约简并对特殊属性编码融合处理,将其结果用于优化概率神经网络(PNN)结构,降低系统复杂度。以PNN作为基分类器,基分类器通过反复迭代、权重更替,然后加权融合处理形成最终的强多分类器。实验结果表明,该方案是有效的态势要素获取方法并且精确度达到95.53%,明显优于
libsvm-3.17
- 为了真实有效地提取网络安全态势要素信息,提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化态势要素获取框架中,根据Agent节点功能的不同,划分为不同的层次。利用主成分分析(Principal Component Analysis, PCA)对训练样本属性进行约简并对特殊属性编码融合处理,按照处理结果改进概率神经网络(Probabilistic Neural Network, PNN)结构,以降低系统复杂度。然后以改进的PNN作为基分类器,结合自适应增强算法,通过基分类器反
GIHS et al
- 用MATLAB编写的基于几种图像融合程序,有GIHS、IHS、PCA等等,还包括测试图像(Several image fusion programs written in MATLAB, including GIHS, IHS, PCA, etc., also include test images)