PNC in 4D Object and Multi-Dimensional Data Modeling
Advances in systems analysis, software engineering, and high performance computing book series · 2017
Proposed method, called Probabilistic Features Combination (PFC), is the method of N-dimensional data interpolation and extrapolation using the set of key points (knots or nodes). The method of Probabilistic Features Combination (PFC) enables interpolation and modeling of high-dimensional data using features' combinations and different coefficients ? as modeling function. Functions for ? calculations are chosen individually at each data modeling and it is treated as N-dimensional probability distribution function: ? depends on initial requirements and features' specifications. PFC method leads to data interpolation as handwriting or signature identification and image retrieval via discrete set of feature vectors in N-dimensional feature space. So PFC method makes possible the combination of two important problems: interpolation and modeling in a matter of image retrieval or writer identification. PFC interpolation develops a linear interpolation in multidimensional feature spaces into other functions as N-dimensional probability distribution functions.