isomds
fun isomds(proximity: Array<DoubleArray>, k: Int, tol: Double = 1.0E-4, maxIter: Int = 200): IsotonicMDS
Kruskal's nonmetric MDS. In non-metric MDS, only the rank order of entries in the proximity matrix (not the actual dissimilarities) is assumed to contain the significant information. Hence, the distances of the final configuration should as far as possible be in the same rank order as the original data. Note that a perfect ordinal re-scaling of the data into distances is usually not possible. The relationship is typically found using isotonic regression.
Parameters
proximity
the non-negative proximity matrix of dissimilarities. The diagonal should be zero and all other elements should be positive and symmetric.
k
the dimension of the projection.
tol
tolerance for stopping iterations.
max
maximum number of iterations.