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Can we apply PCA on categorical data?
While it is technically possible to use PCA on discrete variables, or categorical variables that have been one hot encoded variables, you should not. Simply put, if your variables don’t belong on a coordinate plane, then do not apply PCA to them.
How do you use PCA for feature selection in Python?
Principal Component Analysis (PCA) PCA, generally called data reduction technique, is very useful feature selection technique as it uses linear algebra to transform the dataset into a compressed form. We can implement PCA feature selection technique with the help of PCA class of scikit-learn Python library.
Is PCA a filter method?
PCA is a dimension reduction technique (than direct feature selection) which creates new attributes as a combination of the original attributes in order to reduce the dimensionality of the dataset and is a univariate filter method.
Do you still use PCA for categorical features?
Statistically speaking, categorical features can be seen as discrete random variables in interval [0,1]. Computation for expectation E {X} and variance E { (X-E {X})^2) are still valid and meaningful for discrete rvs. I still stand for the applicability of PCA in case of categorical features.
How is PCA used in principal component analysis?
In simple words, PCA is a method of obtaining important variables (in form of components) from a large set of variables available in a data set. It extracts low dimensional set of features by taking a projection of irrelevant dimensions from a high dimensional data set with a motive to capture as much information as possible.
How to calculate PCA for categorical variables in Python?
The following publication shows great and meaningful results when computing PCA on categorical variables treated as simplex vertices: Niitsuma H., Okada T. (2005) Covariance and PCA for Categorical Variables. In: Ho T.B., Cheung D., Liu H. (eds) Advances in Knowledge Discovery and Data Mining.
Can a PCA be applied to binary data?
Although a PCA applied on binary data would yield results comparable to those obtained from a Multiple Correspondence Analysis (factor scores and eigenvalues are linearly related), there are more appropriate techniques to deal with mixed data types, namely Multiple Factor Analysis for mixed data available in the FactoMineR R package ( AFDM () ).