Linked Questions
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PCA on categorical variables [duplicate]
I am working on a dataset with many categorical variables for a clustering problem. I've done one-hot encoding where a categorical column with 5 levels will become 5 columns, each has the standard ...
2
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1
answer
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PCA with continuous and categorical features [duplicate]
I have a dataset with both continuous and categorical features.
I want to reduce the dimensionality, but cannot apply PCA directly on the dataset because of the categorical features.
One solution I ...
- 143
1
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0
answers
1k
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Categorical Principal Component Analysis [duplicate]
I am planning to perform a Categorical Principal Component Analysis, I have 14 variables with categorical ordinal data (from a 5 point likert scale) and one variable with categorical nominal data. ...
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0
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0
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383
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PCA on Wine data with only one binary data(white/red wine) and other quantitative data [duplicate]
I am working on wine data with the following format:
...
- 281
0
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0
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55
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How I can work with data set contains both numerical and non-numerical features? [duplicate]
I have a dataset contains both numerical and non-numerical columns.
I want to use PCA, is there any way to handle both?
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41
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1
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44k
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Is there Factor analysis or PCA for ordinal or binary data?
I have completed the principal component analysis (PCA), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA), treating data with likert scale (5-level responses: none, a little, ...
- 453
19
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8
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52k
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Clustering of mixed type data with R
I wonder whether it is possible to perform within R a clustering of data having mixed data variables. In other words I have a data set containing both numerical and categorical variables within and I'...
- 3,692
10
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3
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How can I tell that there is no pattern in the PCA results?
I have a 1000+ samples dataset of 19 variables. My objective is to predict a binary variable based on the other 18 variables (binary and continuous). I'm quite confident that 6 of the predicting ...
- 949
22
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3
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1k
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First step for big data ($N = 10^{10}$, $p = 2000$)
Suppose you are analyzing a huge data set at the tune of billions of observations per day, where each observation has a couple thousand sparse and possibly redundant numerical and categorial variables....
- 1,985
5
votes
2
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8k
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How to handle both text and numbers for PCA in R?
I'm relatively new to R and am working with a very large dataset that has a mix of numerical scores (for instance, household income) as well as text values (i.e. race). I was planning on using PCA to ...
- 51
5
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1
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Combining together principal components from PCA performed on different subsets of a large dataset
I'm trying to QA a process in which the data has over a million rows with approx 60,000 variables in a binary form. The aim of the process was to perform k-means clustering, but prior to this, the 60,...
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4
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1
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Is continuous inputs an assumption of factor analysis?
Should we use only continuous inputs for factor analysis (FA)? My data is a mix of continuous and categorical inputs: one of the inputs has only 600, 700 and 1000 as values.
I found that principal ...
- 4,271
5
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1
answer
3k
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PCA or MCA for binarized data
I am working with bioinformatics and I have data that looks like the following:
...
- 481
-1
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2
answers
4k
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Principle Component Analysis on categorical predictors [duplicate]
I tried running prcomp() on my training set, which contains some categorical/factor predictors (as well as a binary response), and was given an error saying my data needs to be numeric. Can PCA not be ...
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3
votes
1
answer
3k
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Why convert categorical data into numerical using one hot encoding
I don't have very strong statistical background, and I'm new in data science...
Now, I am practicing PCA (Principle Component Analysis) for dimension reduction. This tutorial looks very complete, but ...
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