gistfile1.txt

/** @brief the class Multidimensionalscaling is used to perform
 * multidimensional scaling (capable of landmark approximation
 * if requested).
 *
 * Description of classical embedding is given on p.261 (Section 12.1) of
 * Borg, I., & Groenen, P. J. F. (2005).
 * Modern multidimensional scaling: Theory and applications. Springer.
 *
 * Description of landmark MDS approximation is given in
 *
 * Sparse multidimensional scaling using landmark points
 * V De Silva, J B Tenenbaum (2004) Technology, p. 1-4
 * 
 * In this preprocessor the LAPACK routine DSYEVR is used for
 * solving an eigenproblem. If ARPACK library is available,
 * its routines DSAUPD/DSEUPD are used instead.
 *
 * Note that the target dimension should be set with reasonable value
 * (using set_target_dim).  If it is higher than the intrinsic
 * dimensionality of the dataset 'extra' features of the output 
 * might be inconsistent (essentially, having a zero or negative
 * eigenvalue). In this case a warning is shown.
 *
 * Faster landmark approximation is parallel using pthreads.
 * As for choice of landmark number it should be at least 3 for
 * proper triangulation. For reasonable embedding accuracy greater
 * values (30%-50% total number of examples) is pretty good for
 * most tasks.
 */