Description: 基于奇异值分解的核线性判别分析（Kernel Discriminant Analysis via QR Decomposition）。
-based on the singular value decomposition of the nuclear linear discriminant analysis (Kernel Discriminant An alysis via QR Decomposition). Platform: |
Size: 1945 |
Author:李民 |
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Description: Feature scaling for kernel Fisher discriminant analysis using leave-one-out cross validation.
FS-KFDA is a package for implementing feature scaling for kernel fisher discriminant analysis.-Feature scaling for kernel Fisher discrim inant analysis using leave-one-out cross vali dation. FS-KFDA is a package for implementing f eature scaling for kernel fisher discriminant analysis. Platform: |
Size: 510976 |
Author: |
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Description: 基于奇异值分解的核线性判别分析（Kernel Discriminant Analysis via QR Decomposition）。
-based on the singular value decomposition of the nuclear linear discriminant analysis (Kernel Discriminant An alysis via QR Decomposition). Platform: |
Size: 2048 |
Author:李民 |
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Description: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms
and Matlab source codes for the kernel direct discriminant analysis (KDDA) -Face Recognition Using Kernel Direct Disc riminant Analysis Algorithms and Matlab sourc e codes for the kernel direct discriminant anal ysis (KDDA) Platform: |
Size: 744448 |
Author:cy |
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Description: Matlab source codes for the kernel direct discriminant analysis (KDDA),Author: Lu Juwei , Bell Canada Multimedia Lab, Dept. of ECE, U. of Toronto ,Released in 03 September 2003 Platform: |
Size: 748544 |
Author:qiuzhihao |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm Platform: |
Size: 4271104 |
Author:查日东 |
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Description: 基于核的fisher判别分析，中文文献，从各个核心期刊中收集，希望对大家有用-Kernel-based fisher discriminant analysis, English literature, from all the core collection of journals, U.S. hope to be useful Platform: |
Size: 905216 |
Author:宦若虹 |
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Description: 这是一个关于Fisher线性判别分析的Matlab的m文件，给出了在高斯核下的程序源码。-This is a Fisher linear discriminant analysis on the Matlab m-file, given the procedures in the lower-Gaussian source.Kernel Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction. Platform: |
Size: 2048 |
Author:gcl |
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Description: 在AR人脸库上进行DCT变换，使用DCT变换后的图像进行 kernel fisher discriminant analysis,其中kernel 函数可以自己选择-In the AR face database on the DCT transform, using the DCT transformed image kernel fisher discriminant analysis, which can choose the kernel function Platform: |
Size: 2048 |
Author:邵珠立 |
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Description: In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition Platform: |
Size: 2048 |
Author:sofi |
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Description: 提出了基于特征融合和模糊核判别分析(FKDA)的面部表情识别方法。首先，从每幅人脸图像中手工定
位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基
准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进
行特征融合，作为表情识别的输人特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最
近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和Ekman“面部表情图片”数据库上的实验，证实
了所提方法的有效性。-Proposed based on feature fusion and fuzzy kernel discriminant analysis (FKDA) facial expression recognition. First, face images of each piece of hand-set
Bit 34 basis points, as the geometric features of facial expression images, while using Gabor wavelet transform method to transform the images of each piece of expression, and extraction-based
Quasi-point of the Gabor wavelet coefficients, as Gabor features of facial expression image second, using canonical correlation analysis on the geometric features and Gabor features into
Line feature fusion, as expression recognition of input features then, using fuzzy kernel discriminant analysis method to extract and further identification features of expression Finally, the most
Neighbor classifier to complete expression of the classification. International expression by JAFFE database and Ekman "facial image" database on the experiment, confirmed
The proposed method. Platform: |
Size: 375808 |
Author:MJ |
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Description: Kernel Discriminant Analysis a kernel extension of Linear Discriminant Analysis technique which is a well-known feature extraction technique.-Kernel Discriminant Analysis is a kernel extension of Linear Discriminant Analysis technique which is a well-known feature extraction technique. Platform: |
Size: 3072 |
Author:rem |
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