skm_gdp_cpp: skm_gdp_cpp
In skm: Selective k-Means

Description Usage Arguments Details Value

solve selective kmeans via a greedy propagation.

1	skm_gdp_cpp(x, k = 0L)

`x`	an m x n matrix of s - t - dist
`k`	number of index to be selected from x row index start from 0.

skm_gdp_cpp init with an input m x n matrix x and want to select an index set s of size k from x row index started from 0 such that

minimize sum(min(x.subview(i in s, all j), min over all i), sum over all j)

skm_gdp_cpp solve the problem with greedy propagation via selecting the current best addon index from the index set left, addon index is defined as such index when addon to the selected one can bring the most improvement.

since skm_gbp_cpp would select index one by one, and no return, e.g., if select index A for k = 1, then selection on k = 2 would build on k = 1, so index A is always present in the solution, so all index can be ranked w.r.t when it would be considered as the best addon. as a result skm_gbp_cpp a parameter k is not always required, so default k = 0 will resturn a vector of size m, and user can select to top k as solution for k.

s a ranked index 0 - m - 1 where the top k would minimize sum(min(x.subview(i in s(0..k-1), all j), min over all i), sum over all j)

skm documentation built on May 1, 2019, 10:10 p.m.