Description: 主成分分析是把多个指标化为少数几个终合指标的一种统计分析方法。本源代码为matlab中源代码，并添加了相应的分析注解-Principal Component Analysis is more than a few indicators as a final indicator of a statistical analysis method. Source code for which the source code Matlab, and add the corresponding analysis notes Platform: |
Size: 4096 |
Author:东 |
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Description: PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis practice. For the latter part it is assumed (but not
absolutely necessary) that the reader is already familiar with these
methods. It also assumes you have had some preliminary experience
with linear/matrix algebra. Platform: |
Size: 270336 |
Author:郭大 |
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Description: Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.-Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA (X, PPCA_DIM) computes the principal component subspace U of dimension PPCA_DIM using a centred covariancematrix X. The variable VAR contains the off-subspace variance (whichis assumed to be spherical ), while the vector LAMBDA contains thevariances of each of the principal components. This is computedusing the eigenvalue and eigenvector decomposition of X. Platform: |
Size: 1024 |
Author:西晃云 |
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Description: 包含间隔偏最小二乘，组合偏最小二乘，和间隔主成分分析-Contains the interval partial least squares, combination of partial least squares, and spacing of principal component analysis Platform: |
Size: 932864 |
Author:zwerbc |
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Description: Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping. Platform: |
Size: 2048 |
Author:Karthikeyan |
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Description: 主成分分析.mht：介绍主成分分析的文章
edgepick.m：本程序的目的是实现边缘检测
drawprim.m：找到最大主分量所在的位置，从而在原图象中绘出
practical.m：一个主分量的程序，边缘检测和主分量提取
prin.m：该程序是用princomp函数来提取矩阵的主成分-Principal component analysis. Mht: the article describes the principal component analysis edgepick.m: The purpose of this program is to achieve edge detection drawprim.m: to find the location of the maximum principal component, which in the original image in the draw practical.m: a principal components process, edge detection and principal component extraction prin.m: princomp function of the program is to extract the principal component matrix Platform: |
Size: 17408 |
Author:李仕诚 |
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