Open AccessDimensionality reduction methods for Impedance Spectroscopy data of biological materials
Author(s) -
Ricardo Cavalieri,
Pedro Bertemes-Filho
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2008/1/012009
Subject(s) - dimensionality reduction , dielectric spectroscopy , reduction (mathematics) , curse of dimensionality , computer science , data set , artificial neural network , set (abstract data type) , electrical impedance , principal component analysis , data reduction , biological data , algorithm , artificial intelligence , biological system , data mining , mathematics , engineering , chemistry , bioinformatics , geometry , electrode , biology , electrical engineering , electrochemistry , programming language
Electrical impedance spectroscopy combined with Neural Networks can be a powerful combination to identify biological materials. This paper utilizes a data set containing two biological samples taken from different species and applies the most popular methods of dimensionality reduction. This is done in order to find out which method is able to minimize computational demand and maximize accuracy in the classification test. This paper proposes that the classic PCA method is the fastest and the most accurate under the configurations used.