Visualization of a social network analysis (SNA) defined by a high-dimensional parameter space (21 dimensions). Dimensionality reduction (DR) is used here to allow the reduction of the number of variables that defines a system, into a lower-dimensional space (2D).
In this case, I’m using self-organizing maps (SOM), which represent a type of unsupervised artificial neural network, to reduce the high dimensionality of the data whilst also retain the high-dimensional non-linear associations.
Another feature extraction used (side by side image), is a linear feature extraction method -> K-means cluster analysis, (although linear methods don’t retain the associations distributed non-linearly in the high dimensional space).
As the dataset was relatively small, I used only CPU ( 2 x Intel Core i7-4930k @ 3.40GHz) computing for the training phase. If you are dealing with a big data set, a GPU might be a better way to go to train your dataset.
GPU – GPU