Modeling of High-Dimensional Data in Object Recognition
International Journal of Innovative Research in Electronics and Communications · 2017
Proposed method, called Probabilistic Features Combination (PFC), is the method of multidimensional data modeling, extrapolation and interpolation using the set of high-dimensional feature vectors.This method is a hybridization of numerical methods and probabilistic methods.Identification of faces or fingerprints need modeling and each model of the pattern is built by a choice of multi-dimensional probability distribution function and feature combination.PFC modeling via nodes combination and parameter γ as Ndimensional probability distribution function enables data parameterization and interpolation for feature vectors.Multi-dimensional data is modeled and interpolated via nodes combination and different functions as probability distribution functions for each feature treated as random variable: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.