# TrainSuperMDS: Find a set of configuration points that agree with a... In superMDS: Implements the supervised multidimensional scaling (superMDS) proposal of Witten and Tibshirani (2011)

## Description

Given a nxn dissimilarity matrix D and a n-vector of binary (1,2) class labels y, this function outputs a set of configuration points z1,...,zn, each a S-vector, such that the distances between the configuration points approximate the dissimilarity matrix D, AND such that zis >= zjs tends to occur when yi >= yj.

## Usage

 `1` ```TrainSuperMDS(d = NULL, y, alpha = 0.5, S = 2, x = NULL, nstarts = 5, silent = FALSE) ```

## Arguments

 `d` A nxn dissimilarity matrix. If NULL, then x, a nxp data matrix, must be input instead. `y` A n-vector of binary labels, in the form of 1's and 2's. For instance, c(1,1,1,2,2) could be input if D is a 5x5 matrix. `alpha` A scalar between 0 and 1. If alpha=0 then this is just least squares MDS, and if alpha=1 then it's completely supervised. `S` The number of dimensions of the configuration points z1,...,zn. Must be at least equal to 1. `x` A nxp data matrix, to be input only if D is NULL. `nstarts` The supervised MDS algorithm finds a local minimum for the objective. Here, specify the number of initial values to try. If nstarts>1 then the set of configuration points corresponding to the optimal (smallest) value of the objective will be reported. `silent` Set to TRUE in order to turn off printing output to screen.

## Value

 `z` A nxS matrix of the configuration points obtained. `crits` The values of the criterion obtained at the iterations of the algorithm. `stress` The portion of the final criterion value that are due to the STRESS component of the objective function. `super` The portion of the final criterion value that are due to the SUPERVISED component of the objective function.

Daniela M Witten

## References

Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis.

`TestSuperMDS`
 `1` ```# Try ?superMDS for examples ```