Linked Questions
26 questions linked to/from Why PCA of data by means of SVD of the data?
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PCA: When using eigendecompostion instead of single value decompostion [duplicate]
If you want to perform a PCA, I guess that using SVD will always work. Eigendecomposition on the covariance matrix only works when your data is not high dimensional(so n > p). But I'm wonder if there ...
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Relationship between SVD and PCA. How to use SVD to perform PCA?
Principal component analysis (PCA) is usually explained via an eigen-decomposition of the covariance matrix. However, it can also be performed via singular value decomposition (SVD) of the data matrix ...
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Is there an intuitive interpretation of $A^TA$ for a data matrix $A$?
For a given data matrix $A$ (with variables in columns and data points in rows), it seems like $A^TA$ plays an important role in statistics. For example, it is an important part of the analytical ...
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PCA and proportion of variance explained
In general, what is meant by saying that the fraction $x$ of the variance in an analysis like PCA is explained by the first principal component? Can someone explain this intuitively but also give a ...
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What is the difference between R functions prcomp and princomp?
I compared ?prcomp and ?princomp and found something about Q-mode and R-mode principal component analysis (PCA). But honestly – ...
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What is a "kernel" in plain English?
There are several distinct usages:
kernel density estimation
kernel trick
kernel smoothing
Please explain what the "kernel" in them means, in plain English, in your own words.
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Is there any advantage of SVD over PCA?
I know how to calculate PCA and SVD mathematically, and I know that both can be applied to Linear Least Squares regression.
The main advantage of SVD mathematically seems to be that it can be applied ...
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What fast algorithms exist for computing truncated SVD?
Possibly off topic here, but there exist several (one, two) related questions already.
Poking around in the literature (or a google search for Truncated SVD Algorithms) turns up a lot of papers that ...
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Difference between scikit-learn implementations of PCA and TruncatedSVD
I understand the relation between Principal Component Analysis and Singular Value Decomposition at an algebraic/exact level. My question is about the scikit-learn implementation.
The documentation ...
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Steps done in factor analysis compared to steps done in PCA
I know how to perform PCA (principal component analysis), but I would like to know steps that should be used for factor analysis.
To perform PCA, let us consider some matrix $A$, for instance:
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Is PCA still done via the eigendecomposition of the covariance matrix when dimensionality is larger than the number of observations?
I have a $20\times100$ matrix $X$, containing my $N=20$ samples in the $D=100$-dimensional space. I now wish to code up my own principal component analysis (PCA) in Matlab. I demean $X$ to $X_0$ first....
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Understanding the output of SVD when used for PCA [duplicate]
I'm doing principal components analysis (PCA) on quite a bit of data (3000 variables, 100079 data points). I'm doing this mostly for fun; data analysis is not my day job.
Normally, to do a PCA I ...
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PCA vs SVD - understanding difference and preference of SVD over PCA [duplicate]
I understand that PCA and SVD are similar - PCA removes the mean and SVD doesn't? I think I have an understanding of PCA - you would use it to reduce dimensions of data and separate it out into ...
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Princomp() outputs seemingly wrong PCA scores with cor=TRUE input argument
Given a 2D data frame d that is centered and scaled:
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Is there any situation where PCA performs better than SVD?
It's for a text clustering application. There are around 25 documents and 50k features (from TF-IDF), so I was expecting SVD to be a better choice.
I am using sklearn's PCA and TruncatedSVD functions ...
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