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Description: 鲁棒控制器设计，由于RBF网络可以实现任意逼近的非线性关系，它的目标是要做到误差平方和最小，与非线性PCA的目标一致，所以上述非线性PCA的模型可以通过采用两个RBF网络来实现非线性正变换 和反变换 。RBF网络是一个三层前馈网络，隐层采用径向基函数作为激励函数。第一个RBF网络把高维空间的数据映射到低维空间（如图4），第二个RBF网络将前面网络输出的低维空间数据再映射到高维空间，实现数据恢复(如图5)。这两个网络分别进行训练。-robust controller design, as RBF networks can achieve arbitrary nonlinear approximation, Its goal is to achieve the minimum squared error, and nonlinear PCA have the same goal So these nonlinear PCA model may be adopted by two RBF networks to achieve nonlinear transformation and inverse transform. RBF network is a feed-forward network, hidden layer RBF function as an incentive. RBF a network of high-dimensional data mapping space to the low-dimensional space (figure 4), second RBF network will be in front of the output of low-dimensional space mapping data again to a high-dimensional space. data Recovery (figure 5). The two networks separately for training.
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Size: 1549 |
Author: 浇洒距离 |
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Description: PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-PCA (Principal Component Analysis) algorithm has been widely used in engineering and science research, This report mainly from the PCA and the basic structure of the basic tenets of its research, Conventional PCA algorithm used mainly linear algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example
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Size: 454700 |
Author: 东方云 |
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Description: 基于kernel pca的非线性降维算法，原文发表于神经计算杂志上，有兴趣者可以先看论文。-PCA-based kernel of nonlinear reduced dimension algorithm, the original published in the Journal of neural computation, those interested can read papers.
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Size: 1024 |
Author: 武旗 |
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Description: 鲁棒控制器设计，由于RBF网络可以实现任意逼近的非线性关系，它的目标是要做到误差平方和最小，与非线性PCA的目标一致，所以上述非线性PCA的模型可以通过采用两个RBF网络来实现非线性正变换 和反变换 。RBF网络是一个三层前馈网络，隐层采用径向基函数作为激励函数。第一个RBF网络把高维空间的数据映射到低维空间（如图4），第二个RBF网络将前面网络输出的低维空间数据再映射到高维空间，实现数据恢复(如图5)。这两个网络分别进行训练。-robust controller design, as RBF networks can achieve arbitrary nonlinear approximation, Its goal is to achieve the minimum squared error, and nonlinear PCA have the same goal So these nonlinear PCA model may be adopted by two RBF networks to achieve nonlinear transformation and inverse transform. RBF network is a feed-forward network, hidden layer RBF function as an incentive. RBF a network of high-dimensional data mapping space to the low-dimensional space (figure 4), second RBF network will be in front of the output of low-dimensional space mapping data again to a high-dimensional space. data Recovery (figure 5). The two networks separately for training.
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Size: 1024 |
Author: 浇洒距离 |
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Description: PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-PCA (Principal Component Analysis) algorithm has been widely used in engineering and science research, This report mainly from the PCA and the basic structure of the basic tenets of its research, Conventional PCA algorithm used mainly linear algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example
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Size: 454656 |
Author: 东方云 |
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Description: kpca是一种非线性的识别工具，很有实用价值-KPCA is a nonlinear identification tools, very practical value
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Size: 1024 |
Author: wqf |
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Description: 非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
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Size: 1024 |
Author: 王博 |
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Description: 提出一种新的非线性变换的彩色空间 ″″, 利用次高斯概率分布函数拟合皮肤色度信息, 得到候选区
YC C
r b
域。为了排除候选区域中的非人脸, 首先根据均值和方差信息分割出候选区域中的纹理特征信息, 再通过多尺度
)
(
信息定位眼睛, 然后根据人脸特征的几
形态边缘检测算子检测候选区域的边缘, 利用 边缘方向
PCA PCAED
( )
何形状信息定位其他特征 鼻、嘴 , 通过这些几何特征信息对肤色分割得到的候选区域进行验证, 最终得到正确
的人脸区域。利用3 个实验数据集测试该算法, 并与其它相应的算法相比较, 提出的非线性彩色空间对于肤色分
割具有很好的效果, 且对光照和姿态具有良好的不变性。另外, 利用 信息和几何特征信息检测人脸特征
PCAED
具有很高的定位精度, 定位检测率优于其他方法。实验结果表明, 该算法具有定位准确率高, 漏检率和误检率低
等特点。-
A novel approach for skin segmentation and facial feature extraction is proposed
The proposed skin segmentation is a method for integrating the chrominance components of
″″ . ″″
r b r b
nonlinear YC C color model The chrominance components of nonlinear YC C color space
,
are modeled using a subgaussian probability density function and then the face skin is seg
. ,
mented based on this function In order to authenticate the face candidate regions firstly tex
ture information in face candidate regions is segmented using mean and variance of luminance
, . ,
information and then the eye is located by the PCA edge direction information Finally the
, ,
others features such as nose and mouth also are detected using the geometrical shape infor
. 2 ,
mation As all the above mentioned techniques are simple and efficient the skin segmentation
.
based on nonlinear color spacemethod has the invariability of lighting and pose In the experi
, .
ments themethod has been successfull
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Size: 458752 |
Author: zz |
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Description: 主成分分析和偏最小二乘SquaresPrincipal成分分析（ PCA ）和偏最小二乘（ PLS ） ，广泛应用于工具。此代码是为了显示他们的关系，通过非线性迭代偏最小二乘（ NIPALS ）算法。
-Principal Component Analysis and Partial Least SquaresPrincipal Component Analysis (PCA) and Partial Least Squares (PLS) are widely used tools. This code is to show their relationship through the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm.
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Size: 380928 |
Author: harrison |
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Description: This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation-This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation
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Size: 226304 |
Author: tra ba huy |
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Description: 一共包含了5个ICA的算法，其中：
fastica.m文件中的ICA算法是基于负熵的；
m_fastica.m文件中的ICA算法是基于负熵的改进算法；
fastica_kurt.m文件中的ICA算法是基于峭度的；
fastica_ML.m文件中的ICA算法是基于互信息的；
NLPCA.m文件中的ICA算法是基于非线性PCA的。-Contains a total of five ICA algorithm, in which: fastica.m file in the ICA algorithm is based on negative entropy m_fastica.m file in the ICA algorithm is based on negative entropy of the improved algorithm fastica_kurt.m file in the ICA algorithm is Based on Kurtosis fastica_ML.m file in the ICA algorithm is based on mutual information NLPCA.m file in the ICA algorithm is based on nonlinear PCA' s.
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Size: 5120 |
Author: nifeng |
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Description: Kernel PCA toy example Nonlinear component analysis as a kernel Eigenvalue problem
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Size: 1024 |
Author: henry |
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Description: 1.BP神经网络进行模式识别
2.用BP网络对非线性系统进行辨识
3.一个神经网络PID控制器
4.图像处理的PCA算法
5.图像处理的穷举算法-1.BP neural network pattern recognition 2. Using BP network identification of nonlinear systems 3. A neural network PID controller 4. The PCA algorithm for image processing 5. The exhaustive algorithm for image processing,
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Size: 5120 |
Author: hudingyin |
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Description: nonlinear pca的具体实现代码-nonlinear pca concrete realization of the code
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Size: 158720 |
Author: 华强 |
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Description: 基于非线性PCA准则的两个盲信号分离算法，
利用矩阵广义逆递推得到一种盲信号分离算法，采用梯度下降算法和线性寻优而得到另一种自适应盲信号分离算法-sourceseparation based nonlinear pca princple
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Author: 张希文 |
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Description: 这是外国人实现的非线性主成份分析，可下载相应的文章，可用来降维！-Applies the kernel method to unsupervised algorithms as for instance Principal Component Analysis. This gives a principled and efficient approach to nonlinear PCA
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Size: 1024 |
Author: zzgzzg99 |
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Description: test an exemple of nonlinear system using kpca and pca
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Size: 1024 |
Author: hamza nejib bennour |
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Description: FACE RECOGNITION BASED ON NONLINEAR PCA
In order to obtain the complete source code for FACE RECOGNITION BASED ON
NONLINEAR PCA-FACE RECOGNITION BASED ON NONLINEAR PCA
In order to obtain the complete source code for FACE RECOGNITION BASED ON
NONLINEAR PCA
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Size: 464896 |
Author: Azhar |
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Description: 非线性pca，基于核的pca,并配有相关图形，非常适合学生等入门级别的开发者-Nonlinear pca, based nuclear pca, and with related graphics, very suitable for students and other entry-level developers
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Size: 140288 |
Author: z |
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Description: 压缩文件夹中主要包含用于非线性主成分分析的程序(Nonlinear PCA toolbox for MATLAB)
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Size: 137216 |
Author: guduxingzhe11
|
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