Methods to reduce dimensionality within a feature? Suppose that I am interested in predicting an outcome (say, the arrival delay [in seconds] of a flight) based upon a set of features. 
One of these features is a nominal variable - carrier - that specifies the airline carrier of the flight. This feature has 16 different values. After investigation of how arrival delay is distributed across each carrier, it appears that some carriers could be collapsed into one value (e.g., "AS" and "HA" or "WN" and "B6").

install.packages("nycflights13")
library(nycflights)

boxplot(
    formula = arr_delay ~ with(flights, reorder(carrier, -arr_delay, median, na.rm = TRUE)),
    data = flights,
    horizontal = TRUE, 
    las = 2,
    plot = TRUE
)




In general, are there well-known methods for reducing the dimensionality within a feature?
You could try clustering the data with the carrier feature removed, add it back in, and look at the distributions of the carrier feature per cluster.