Are there any methods of supervised learning that return a bitmap instead of a set of parameters?
For example, the SVM or ANN methods perform search of a surface which would separate the data points in a best way. This surface is returned in the vector or parametric form. Are there methods returning a spatial bitmap each voxel of which contains a numeric value defining a class for all points lying within a given voxel?
I would like to share some of the results of my attempts in this direction. Since I'm relatively new in machine learning I can't be quite sure that this was not done before. Perhaps there are some reasons that are obscure to me which make this approach meaningless for a real data.
The below images were obtained by representing each data point by RBF and calculating influence of all such RBFs for each voxel. The adaptive grid was used to optimize calculations. Taking the first step in this direction, I received encouraging performance results with the dummy data and with the one real dataset. Obviously, more tests are required to draw conclusions about the effectiveness of this method. Perhaps, this approach will be useful for noise reduction and datasets with complicated clusters, also it is capable of processing data points in batch mode having no significant impact to a final result.
