Linked Questions
35 questions linked to/from Should one remove highly correlated variables before doing PCA?
41
votes
2
answers
20k
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How does Factor Analysis explain the covariance while PCA explains the variance?
Here is a quote from Bishop's "Pattern Recognition and Machine Learning" book, section 12.2.4 "Factor analysis":
According to the highlighted part, factor analysis captures the covariance between ...
- 3,295
15
votes
3
answers
28k
views
What are the assumptions of factor analysis?
I want to check if I really understood [classic, linear] factor analysis (FA), especially assumptions that are made before (and possibly after) FA.
Some of the data should be initially correlated and ...
- 323
21
votes
3
answers
54k
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When can we speak of collinearity
In linear models we need to check if a relationship exists among the explanatory variables. If they correlate too much then there is collinearity (i.e., the variables partly explain each other). I am ...
- 795
21
votes
3
answers
33k
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Do I need to drop variables that are correlated/collinear before running kmeans?
I am running kmeans to identify clusters of customers. I have approximately 100 variables to identify clusters. Each of these variables represent the % of spend by a customer on a category. So, if I ...
- 211
11
votes
3
answers
27k
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Can the scaling values in a linear discriminant analysis (LDA) be used to plot explanatory variables on the linear discriminants?
Using a biplot of values obtained through principal component analysis, it is possible to explore the explanatory variables that make up each principle component. Is this also possible with Linear ...
- 1,683
7
votes
3
answers
1k
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Are there examples of more informative PCA plots? [duplicate]
I am often disappointed with PCA plots in the scientific literature. Typically PCA plots do not provide a breakdown of the variables and their weights, just something like PCA1 (70% variance explained)...
- 273
11
votes
1
answer
6k
views
How to interpret this PCA biplot coming from a survey of what areas people are interested in?
Background: I asked hundreds of participants in my survey how much they are interested in selected areas (by five point Likert scales with 1 indicating "not interested" and 5 indicating "interested").
...
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4
votes
1
answer
11k
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What are the units in this PCA biplot? [duplicate]
This is a plot of my data
These are the values:
...
- 151
5
votes
4
answers
4k
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Can PCA allow to identify redundant variables that can be removed before doing cluster analysis?
I hope this is suitable for this forum: I am new to PCA and what I ultimately want to do is perform cluster analysis on my dataset.
I have 20 physical descriptor variables for organisms, each with 300 ...
pca_newby
4
votes
4
answers
1k
views
PCA, dimensionality, and k-means results: reaction to duplicating of variables
There are many excellent conversations on CV about the curse of dimensionality when applied to methods like k-means. The answer in the same post and other research (e.g., the paper titled "When Is ‘...
- 496
6
votes
2
answers
1k
views
Which font to use in a plot to maximize clarity?
I was reading the Wikipedia page of the Bitstream Vera font (used by default by matplotlib), which says:
The Bitstream Vera Sans Mono typeface in particular is suitable for
technical work, as it ...
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20
votes
1
answer
3k
views
Geometric understanding of PCA in the subject (dual) space
I am trying to get an intuitive understanding of how principal component analysis (PCA) works in subject (dual) space.
Consider 2D dataset with two variables, $x_1$ and $x_2$, and $n$ data points (...
- 100k
5
votes
1
answer
4k
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What can be the reason to do feature selection based on variance before doing PCA?
I have noticed that when applying PCA to large datasets, people often will first subset the data considerably. Sometimes people just randomly take a subset of the features/variables, but often they ...
- 433
5
votes
1
answer
2k
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Interpreting overlapping arrows on a PCA biplot: does it mean that the variables are redundant?
I'm new in principal component analysis (PCA) and I don't really understand the biplot representation of its results, so I would really appreciate some guidance. Having the example of the illustration ...
- 51
4
votes
1
answer
2k
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Does it make sense to use PCA when the determinant of the correlation matrix is (almost) zero?
I'm running a PCA over a data set of $N \times p$ size ($N\approx 1000$ being the number of measurements and $p\approx 200$ being the number of dimensions/predictors).
I expect many of the predictors ...
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