Select the Most Informative Components
Takes an array of observations as an input and outputs a subset of the components having the most extreme kurtoses.
selectComponents(x, first = 2, last = 2)
Numeric array of an order at least two. It is assumed that the last dimension corresponds to the sampling units.
Number of components with maximal kurtosis to be selected. Can equal zero but the total number of components selected must be at least two.
Number of components with minimal kurtosis to be selected. Can equal zero but the total number of components selected must be at least two.
In independent component analysis (ICA) the components having the most extreme kurtoses are often thought to be also the most informative. With this viewpoint in mind the function
selectComponents selects from
first components having the highest kurtosis and
last components having the lowest kurtoses and outputs them as a standard data matrix for further analysis.
Data matrix with rows corresponding to the observations and the columns correponding to the
last selected components in decreasing order with respect to kurtosis. The names of the components in the output matrix correspond to the indices of the components in the original array
1 2 3 4 5 6 7 8 9 10 11 12
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.