With application in fast data mining
Elham Bavafaye Haghighi, Mohammad Mahdi Rahmati, Günther Palm · 2013
Reducing Computational complexity is a major issue in data mining. Mapping to Multidimensional Optimal Regions (M2OR) is a special purposed method for multiclass classification task. It reduces computational complexity in comparison to the other concepts of classifiers. In this paper, the accuracy of M2OR increases using Learning Inductive Riemannian Manifold in Abstract from (LIRMA). LIRMA estimates the underlying structure of a dataset with respect to the embedded dynamical system of data. The estimated non-linear mapping of LIRMA has the advantage of being topology preserving and inductivity. As a result, the optimal regions are determined more precisely. Consequently, the accuracy of M2OR increases and the memory complexity decreases accordingly.