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I. INTRODUCTION of PCA
1. Related background
Principal component Analysis (Principal Component ANALYSIS,PCA) is a statistical method. An orthogonal transformation transforms a set of variables that may be related to a set of linea
Principal Component Analysis (PCA) is a multivariate statistical analysis method that uses linear transformation to select a small number of important variables. It is also called Main Component analysis. In practice, many variabl
1.PCAUsage Scenario: Principal component analysis is a data dimensionality reduction that converts a large number of related variables into a small set of unrelated variables called principal components.Steps:
Data preprocessing (guaranteed no missing values in the data)
Select a factor model (whether PCA
expression:The expression of the above-loaded J is obtained:Then using the Laplace multiplier (a little bit here), we can get the expression of the projection base we want:Here is another characteristic value expression, we want the first m vector is actually here the largest m eigenvalues corresponding to the eigenvector. Prove this can also see, we J can be translated into:That is, when the error J is composed of the smallest d-m eigenvalues, J obtains the minimum value. It's the same as the
expression:The expression of the above-loaded J is obtained:Then using the Laplace multiplier (a little bit here), we can get the expression of the projection base we want:Here is another characteristic value expression, we want the first m vector is actually here the largest m eigenvalues corresponding to the eigenvector. Prove this can also see, we J can be translated into:That is, when the error J is composed of the smallest d-m eigenvalues, J obtains the minimum value. It's the same as the
expression:The expression of the above-loaded J is obtained:Then using the Laplace multiplier (a little bit here), we can get the expression of the projection base we want:Here is another characteristic value expression, we want the first m vector is actually here the largest m eigenvalues corresponding to the eigenvector. Prove this can also see, we J can be translated into:That is, when the error J is composed of the smallest d-m eigenvalues, J obtains the minimum value. It's the same as the
History of principal component analysis:Pearson was proposed in 1901 and then developed by Hotelling (1933) as a multivariate statistical method. Through the main components of the analysis to show the largest individual differences, but also to reduce the number of regression analysis and clustering variables, you can
calculation is used in many places, such as image recognition, PageRank, Lda, and PCA, which will be mentioned later.
It is a feature face that is widely used in image recognition. Feature face extraction has two purposes: first, to compress data. For an image, you only need to save the most important part, and thenProgramIt is easier to process. When extracting the main features, a lot of noise is filtered out. The role of PCA is very relevant.
There are many methods to evaluate the fe
indicators will lose a lot of information, easy to produce erroneous conclusions.
It is therefore necessary to find a reasonable way to minimize the loss of information contained in the original indicator while minimizing the need for analysis in order to achieve the objective of a comprehensive analysis of the data collected. Because there is a certain correlation between the variables, it is possible to
interested in the average luminance value of an image block, so we can subtract this value to make the mean normalized.The specific step is that if you represent the luminance (grayscale) value () of a 16x16 image block, you can use the following algorithm to perform a 0-value operation on each image:, for allPlease note: 1) for each input image block to perform the above two steps, 2) here is the average luminance value of the image block. It is particularly important to note that this and the
Abstract:
PCA (principal component analysis) is a multivariate statistical method. PCA uses linear transformation to select a small number of important variables. It can often effectively obtain the most important elements and structures from overly "rich" data information, remove Data Noise and redundancy, and reduce the original complex data dimension, reveals
Module Name: pca.pyPCA principle and the principle of tightening techniques to be mended ...#-*-coding:utf-8-*-" "Created on March 2, 2015 principal component analysis of p-14 image of @author:ayumi Phoenixch01" " fromPILImportImageImportNumPydefPCA (X):"""principal component
PCA, principal component analysis Principal component analysis is mainly used for dimensionality reduction of data. The dimensions of the data features in the raw data may be many, but these characteristics are not necessarily im
The contents of this lesson:
Factor analysis
The derivation process of EM step in---factor analysis
Principal component analysis: an effective way to reduce dimensions
the problem of mixed Gaussian model with
factor
Principal factor analysis, mentioned in the refining into gold course:?A method of dimensionality reduction is the generalization and development of principal component analysis.?is a statistical model used to analyze the effects of factors behind surface phenomena. An attem
Principal component Analysis (principal components ANALYSIS,PCA) is a simple machine learning algorithm, the main idea is to reduce the dimension of high-dimensional data processing, to remove redundant information and noise in the data.Algorithm:Input sample: D={x1,x2,⋯,xm}
formula for X by Y is as follows:
X ' = Aky +mx (2.4)
At this time cy = diag (λ1,λ2,..., λk), the mean square error between x and X. can be expressed by the following formula:
Λk+1+.λk+2...+λn (2.5) (No Formula editor AH)
Above we mentioned that for the eigenvalues λ is from large to small sort, then this time through the equation 2.5 can be shown by selecting K has the largest eigenvalue of the eigenvector to reduce the error. Therefore, the K-L transformation is the best transformatio
Principal component analysis and exploratory factor analysis are common methods used to explore and simplify multivariable complex relationships, which can solve the problem of multivariable data with over-complexity of information.PCA: A data dimensionality reduction technique that transforms a large number of related
The principal component analysis method is to transform the existing correlation primitive variable into unrelated new variable by orthogonal transformation, discard the variable with low contribution, the contribution degree can be understood as the variance of the variable, the greater the variance, the higher the contribution degree, the information of the var
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