Hands-On Machine Learning with C++

Sammon mapping

Sammon mapping is one of the first non-linear dimensionality reduction algorithms. In contrast to traditional dimensionality reduction methods, such as PCA, Sammon mapping does not define a data conversion function directly. On the contrary, it only determines the measure of how well the conversion results (a specific dataset of a smaller dimension) correspond to the structure of the original dataset. In other words, it does not try to find the optimal transformation of the original data; instead, it searches for another dataset of lower dimensions with a structure that's as close to the original one as possible. The algorithm can be described as follows. Let's say we have -dimensional vectors, . Here, vectors are defined ...