  An incremental version of the ELMVIS+ method is proposed in this paper. It
iteratively selects a few best fitting data samples from a large pool, and adds
them to the model. The method keeps high speed of ELMVIS+ while allowing for
much larger possible sample pools due to lower memory requirements. The
extension is useful for reaching a better local optimum with greedy
optimization of ELMVIS, and the data structure can be specified in
semi-supervised optimization. The major new application of incremental ELMVIS
is not to visualization, but to a general dataset processing. The method is
capable of learning dependencies from non-organized unsupervised data -- either
reconstructing a shuffled dataset, or learning dependencies in complex
high-dimensional space. The results are interesting and promising, although
there is space for improvements.
