Dimensionality Reduction Techniques Ordinal Variables on different scales I have a data set that is a mix between Ordinal variables and numerical variables.
The problem is that the ordinal variables are on different scales, such as 0-2, 1-4. The data set has 35 variables. I want to perform some type of data reduction on the data set but am unsure of what to do given the different scales.
Techniques such as:

*

*Random Forest

*Principal Component Analysis

However, I am not sure if it's possible due to the different scales?
I am using pandas.

There is a paper that discusses a Bayesian factor analysis for mixed ordinal and continuous variables:
Kevin M. Quinn. 2004. “Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses.” Political Analysis. 12: 338-353.
This is implemented in the MCMCpack package in R: MCMCmixfactanal().
Another option would be the generalized low rank model (GLRM) which is implemented in h2o.  This is similar in spirit to PCA, but without the linearity assumption (and it also permits missing data).
