Hands-On Machine Learning with C++

Multidimensional scaling

Multidimensional scaling (MDS) can be considered as an alternative to factor analysis when, in addition to the correlation matrices, an arbitrary type of object similarity matrix can be used as input data. MDS is not so much a formal mathematical procedure but rather a method of efficiently placing objects, thus keeping an appropriate distance between them in a new feature space. The dimension of the new space in MDS is always substantially less than the original space. The data that's used for analysis by MDS is often obtained from the matrix of pairwise comparisons of objects. The main MDS algorithm's goal is to restore the unknown dimension, , of the analyzed feature space and assign coordinates to each object ...