Linked Questions
41 questions linked to/from How does centering the data get rid of the intercept in regression and PCA?
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PCA demeaning the data [duplicate]
What is the motivation for demeaning the data when doing PCA. I've been told to do it, but I've never heard a good and/or intuitive reason for it. Is this a case where doing it just makes the math ...
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PCA on non-centered data [duplicate]
How does the mean influence PCA?
What happens if I use PCA on data with a mean $\ne0$?
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2
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1
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Is mean centering required in regression? if so, what does it do? [duplicate]
Let say we have a dataset, $\mathbf{X}$ of $m$ instances, and $n$ features, and a target scalar variable $\mathbf{y}$ ($m$ instances).
Now I want to do a regression so, I try to fit a hyperplane $ y =...
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What does it mean to compute eigenvectors of a covariance matrix if the data were not centered first? [duplicate]
Say $\mathbf{X} \in \mathbb{R}^{n \times p}$ and $\boldsymbol{\Sigma} = \frac{1}{n}\mathbf{X}'\mathbf{X}$. The eigenvector decomposition of $\boldsymbol{\Sigma}$ gives $\boldsymbol{\Sigma} = \mathbf{P}...
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PCA Why covariance matrix? [duplicate]
At PCA why we find the Eigenvalues of the covariance matrix and not the eigenvalues of the matrix $A\times A^T$, where $A$ is the data matrix and $A^T$ its transpose? I saw a professor at YouTube who ...
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Centering variables before running PCA [duplicate]
I am learning about PCA, regarding PCA I need to know that given a dataset is it always necessary to use centering? what if I don't center the variables used in PCA?
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220
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How exactly does one “control for other variables”?
Here is the article that motivated this question: Does impatience make us fat?
I liked this article, and it nicely demonstrates the concept of “controlling for other variables” (IQ, career, income, ...
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61
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How does centering make a difference in PCA (for SVD and eigen decomposition)?
What difference does centering (or de-meaning) your data make for PCA? I've heard that it makes the maths easier or that it prevents the first PC from being dominated by the variables' means, but I ...
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37
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3
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PCA on correlation or covariance: does PCA on correlation ever make sense? [closed]
In principal component analysis (PCA), one can choose either the covariance matrix or the correlation matrix to find the components (from their respective eigenvectors). These give different results (...
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43
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1
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Doing principal component analysis or factor analysis on binary data
I have a dataset with a large number of Yes/No responses. Can I use principal components (PCA) or any other data reduction analyses (such as factor analysis) for this type of data? Please advise how I ...
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Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained to be orthogonal?
I am trying to understand some descriptions of PCA (the first two are from Wikipedia), emphasis added:
Principal components are guaranteed to be independent only if the data set is jointly normally ...
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23
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1
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Need for centering and standardizing data in regression
Consider linear regression with some regularization:
E.g. Find $x$ that minimizes $||Ax - b||^2+\lambda||x||_1$
Usually, columns of A are standardized to have zero mean and unit norm, while $b$ is ...
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User segmentation by clustering with sparse data
Imagine that I have 100k users and 1k categories. For each user, up to 5 categories, I know how much money they have spent. Obviously my data is very sparse.
Now I want to group users by the money ...
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Why does a PCA component have negative values when all inputs are strictly positive? [closed]
Let's say I have X1, X2, X3, and X4. All four variables are strictly positive (no values below zero). The variables are on different scales.
I do a PCA on the four variables' correlation matrix and ...
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6
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Should I standardize or normalize variables before conducting a principal components analysis
I am very confused as I am reading through PCA. Some sources say that I should normalize my data before applying PCA, and some sources say that I should standardize my data before applying PCA. I know ...
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