Applied Research Frontiers
Title
Interpolation and Modeling of Multidimensional Data with Applications
Authors
Dariusz Jacek Jakóbczak
Department of Electronics and Computer Science, Koszalin University of Technology, Sniadeckich 2, 75-453 Koszalin, Poland.
*Corresponding author E-mail address: dariusz.jakobczak@tu.koszalin.pl
Article History
Publication details: Received: 26th April 2022; Revised: 25th May 2022; Accepted: 25th May 2022; Published: 20th June 2022
Cite this article
Jakóbczak D. Interpolation and Modeling of Multidimensional Data with Applications. Appl. Res. Front., 2022, 1(2), 1-9.
Abstract
Proposed method, called Probabilistic Features Combination (PFC), is the method of multi-dimensional 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 N-dimensional 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.
Keywords
image retrieval; pattern recognition; data modelling; vector interpolation; PFC method; feature reconstruction; probabilistic modeling
