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kpca_toy
- 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.
lab432.rar
- 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. Thi
NLCPCA
- nonlinear pca的具体实现代码-nonlinear pca concrete realization of the code
pca
- 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
kpca_origin
- Kernel PCA toy example Nonlinear component analysis as a kernel Eigenvalue problem
five
- 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 process
KPCA
- KPCA是一种基于核的主要成分分析,是一种由线性到非线性之间的桥梁。通过非线性函数把输入空间映射到高维空间,在特征空间中间型数据处理,引入核函数,把非线性变换后的特征空间内积运算转换为原始空间的核函数计算。 基本思想是通过某种隐士方法将输入空间映射到某个高维空间(特征空间),并在特征空间实现PCA。对该算法进行了详细的说明-KPCA is a kernel-based principal components analysis, is a bridge between the linear
kPCA_v2.0
- Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensiona
ndfa_control_1.0.1.tar
- 用于盲辨识的非线性动力因素分析,可用于PCA和BSS的非线性动态状态空间模型。-Nonlinear dynamic analysis of the factors used to blind identification can be used nonlinear dynamic state-space model of PCA and BSS.
Test-kpca-pca
- test an exemple of nonlinear system using kpca and pca
nlpcafaceprot
- 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 RECOGNITIO
20494855sime-kpca
- kpca是对原来的pca程序进行核处理,具有一定的非线性。-Kpca is nuclear processing, on the original pca program is nonlinear.
fquiitwz
- 包括主成分分析、因子分析、贝叶斯分析,包括 MUSIC算法,ESPRIT算法 ROOT-MUSIC算法,数据模型归一化,模态振动,结合PCA的尺度不变特征变换(SIFT)算法,插值与拟合,解方程,数据分析,基于分段非线性权重值的Pso算法。- Including principal component analysis, factor analysis, Bayesian analysis, Including the MUSIC algorithm, ESPRIT algorithm ROOT
rwaznczt
- 是学习PCA特征提取的很好的学习资料,三相光伏逆变并网的仿真,毕业设计有用,基于分段非线性权重值的Pso算法,有信道编码,调制,信道估计等。-Is a good learning materials to learn PCA feature extraction, Three-phase photovoltaic inverter and network simulation, Graduation useful Based on piecewise nonlinear weight value
vxdjfcsm
- 在matlab环境中自动识别连通区域的大小,基于分段非线性权重值的Pso算法,有较好的参考价值,Matlab实现界面友好,脉冲响应的相关分析算法并检验,借鉴了主成分分析算法(PCA),对HARQ系统的吞吐量分析,通过虚拟阵元进行DOA估计。- Automatic identification in the matlab environment the size of the connected area, Based on piecewise nonlinear weight value Pso
qfsiksbr
- 随机调制信号下的模拟ppm,详细画出了时域和频域的相关图,大学数值分析算法,迭代自组织数据分析,基于分段非线性权重值的Pso算法,采用偏最小二乘法,借鉴了主成分分析算法(PCA)。-Random ppm modulated analog signal under Correlation diagram shown in detail the time domain and frequency domain, University of numerical analysis algorithms,
btqwzhux
- 对于初学者具有参考意义,基于分段非线性权重值的Pso算法,包括主成分分析、因子分析、贝叶斯分析,自己编的5种调制信号,Relief计算分类权重,包含特征值与特征向量的提取、训练样本以及最后的识别,是学习PCA特征提取的很好的学习资料。- For beginners with a reference value, Based on piecewise nonlinear weight value Pso algorithm, Including principal component analys
NLCPCA
- 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
Nonlinear PCA toolbox for MATLAB
- 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)
function_kpca
- 使用核函数,在matlab环境下实现非线性主成分分析(Using kernel function to realize nonlinear principal component analysis in Matlab environment.)