skm_mls_cpp

an m x n matrix often m < n, as a convention index rows of x with s, and
cols of x with t so x(i, j) can be expressed as (s_i, t_j) equally.

number of index to be selected from x row index start from 0.

stratify structure, often info on grouping of v so that algorithm should
make random initialization from stratified sample across groups.

an index vector set should be selected before selecting other index.

s_must

max number of iterations can run for optimizing result.
max number of iterations within a single initial run on optimal path.

max_it

max number of attempts or repeats on running for optimial results,
max number of random initialization for finding optimial results.

max_at


solve skm with multiple runs in serial and return all w. optim
  and s_init stratified sampled w.r.t g


Algorithms for solving selective k-means problem,
which is defined as finding k rows in an m x n matrix such that
the sum of each column minimal is minimized.
In the scenario when m == n and each cell value in matrix is a
valid distance metric, this is equivalent to a k-means problem.
The selective k-means extends the k-means problem in the sense
that it is possible to have m != n, often the case m < n which
implies the search is limited within a small subset of rows.
Also, the selective k-means extends the k-means problem in the
sense that the instance in row set can be instance not seen in
the column set, e.g., select 2 from 3 internet service provider
(row) for 5 houses (column) such that minimize the overall cost
(cell value) - overall cost is the sum of the column minimal of
the selected 2 service provider.

skm_mls_cpp: skm_mls_cpp

Description

Usage

Arguments

Value

Details

See Also