Description: 现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher. Platform: |
Size: 2048 |
Author:小弟 |
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Description: KPCA主要在图像去噪声方面有应用。此外还可以进行特征提取，降维使用 -KPCA major noise in the image to have the application. In addition can also be used for feature extraction, dimensionality reduction using Platform: |
Size: 1024 |
Author:videohu |
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Description: AAM是一种有效的人脸特征提取算法 该论文对它进行了改进 相信会对人脸表情识别领域的朋友有很大帮助-AAM is an effective facial feature extraction algorithm of the paper believe it will improve the identification of areas of Facial Expression friends are very helpful Platform: |
Size: 577536 |
Author:张波 |
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Description: 核主成分分析的特征提取的客户流失预测,大家分享一下.-Kernel Principal Component Analysis of feature extraction of customer churn prediction, to share with Members. Platform: |
Size: 446464 |
Author:何生 |
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Description: 快速的人脸特征提取算法KPCA，比普通的pca特征提取算法在效率上好了不少-Fast facial feature extraction algorithm KPCA, than ordinary PCA feature extraction algorithm in the efficiency of a good many Platform: |
Size: 1024 |
Author:songy |
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Description: 一个很好的PCA程序。它可用于数据的降维，消噪及特征提取。-A good PCA procedures. It can be used for data dimensionality reduction, de-noising and feature extraction. Platform: |
Size: 2048 |
Author:xiaolinzi |
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Description:
在特征提取阶段，研究了PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA等多
种方法。不同于基于图象向量的PCA特征提取，由于2DPCA, (2D) ZPCA, DiagPCA和
DiagPCA-I-2DPCA的特征提取都直接基于图象矩阵，计算量小，所以特征的提取速度明
显高于PCA方法。-In the feature extraction stage, the study of the PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA and other methods. Vector is different from the PCA-based image feature extraction, as 2DPCA, (2D) ZPCA, DiagPCA and DiagPCA-I-2DPCA the feature extraction are directly based on image matrix, a small amount of calculation, so the speed of feature extraction method was significantly higher than PCA . Platform: |
Size: 45056 |
Author:付采 |
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Description: KPCA与SVM共同用于人脸识别 SVM提高了分类效果 KPCA是一种借鉴SVM中核函数的一种较好的特征提取方法-KPCA and SVM for face recognition SVM together to improve the classification results from KPCA is a kernel function in SVM a better feature extraction method Platform: |
Size: 224256 |
Author:付赛男 |
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Description: 基于PSO训练SVM的人脸识别
利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径-PSO-based SVM for face recognition training using support vector machine learning ability in the performance of good performance, combined with KPCA feature extraction method, applied to face recognition, the method in experiments to identify the performance of a good performance for the field of face recognition has provided a new way to identify Platform: |
Size: 1097728 |
Author:彭伟 |
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Description: 这是一段简单的基于主元分析法的特征提取的程序-This is a simple method based on principal component analysis of the feature extraction procedure Platform: |
Size: 157696 |
Author:mmy |
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Description: 介绍了一种非常实用的特征提取新方法，针对稀疏核主成分分析方法在特征提取中的不足, 提出了一种基于核K- 均值聚类的稀疏核主成分分析( Sparse KPCA) 的特征提取方法用于说话人识别。-Introduced a very useful new method of feature extraction for Sparse Kernel Principal Component Analysis in Feature Extraction of the lack of a kernel-based K-means clustering of sparse kernel principal component analysis (Sparse KPCA) of the feature extraction methods for speaker recognition. Platform: |
Size: 122880 |
Author:毋桂萍 |
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Description: 别人的东西，有关KPCA特征提取的，看过了，很好很强大-Other people' s things, the KPCA feature extraction, and seen, very good very strong Platform: |
Size: 55198720 |
Author:刘媛 |
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Description: 核主成分分析方法，是主成分分析的一种改进算法，是一种非线性的特征提取方法。
-Kernel principal component analysis, is the principal component analysis of an improved algorithm, is a nonlinear feature extraction method. Platform: |
Size: 1024 |
Author:叶子 |
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Description: 用于人脸识别特征提取的KPCA算法，很好的程序，有问题大家交流-KPCA for face recognition feature extraction algorithm, a very good program, there are problems we share Platform: |
Size: 1024 |
Author:keke |
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Description: KPCA主要在图像去噪声方面有应用。此外还可以进行特征提取，降维使用.-KPCA major noise in the image to have the application. You can also feature extraction using dimension reduction. Platform: |
Size: 2048 |
Author:lp |
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Description: 通过核 K- 均值聚类的方法对语音帧进行聚类 , 由于聚类的中心能够很好地代表类内的特征, 用中心样本帧取代该类, 减少了核矩阵的维数, 然后再采用稀疏 KPCA方法对核矩阵进行特征提取。-Through the nuclear K-means clustering method for clustering of speech frames, the cluster center can be a good representative of the class characteristics of the sample frame to replace the class with the center, reducing the dimension of the nuclear matrix, and then use Sparse KPCA method for feature extraction of the nuclear matrix. Platform: |
Size: 185344 |
Author:piano |
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