TY - JOUR

T1 - Some considerations on physical analysis of data

AU - Wu, Zhaohua

AU - Huang, Norden E.

AU - Chen, Xianyao

PY - 2011/4

Y1 - 2011/4

N2 - In this paper, we present some general considerations about data analysis from the perspective of a physical scientist and advocate the physical, instead of mathematical, analysis of data. These considerations have been accompanying our development of novel adaptive, local analysis methods, especially the empirical mode decomposition and its major variation, the ensemble empirical mode decomposition, and its preliminary mathematical explanations. A particular emphasis will be on the advantages and disadvantages of mathematical and physical constraints associated with various analysis methods. We argue that, using data analysis in a given temporal domain of observation as an example, the mathematical constraints imposed on data may lead to difficulties in understanding the physics behind the data. With such difficulties in mind, we promote adaptive, local analysis method, which satisfies fundamental physical principle of consequent evolution of a system being not able to change the past evolution of the system. We also argue, using the ensemble empirical mode decomposition as an example, that noise can be helpful to extract physically meaningful signals hidden in noisy data.

AB - In this paper, we present some general considerations about data analysis from the perspective of a physical scientist and advocate the physical, instead of mathematical, analysis of data. These considerations have been accompanying our development of novel adaptive, local analysis methods, especially the empirical mode decomposition and its major variation, the ensemble empirical mode decomposition, and its preliminary mathematical explanations. A particular emphasis will be on the advantages and disadvantages of mathematical and physical constraints associated with various analysis methods. We argue that, using data analysis in a given temporal domain of observation as an example, the mathematical constraints imposed on data may lead to difficulties in understanding the physics behind the data. With such difficulties in mind, we promote adaptive, local analysis method, which satisfies fundamental physical principle of consequent evolution of a system being not able to change the past evolution of the system. We also argue, using the ensemble empirical mode decomposition as an example, that noise can be helpful to extract physically meaningful signals hidden in noisy data.

KW - adaptivity

KW - empirical mode decomposition

KW - ensemble empirical mode decomposition

KW - global domain analysis

KW - locality

KW - noise-assisted data analysis

KW - Physical analysis of data

UR - http://www.scopus.com/inward/record.url?scp=80052612013&partnerID=8YFLogxK

U2 - 10.1142/S1793536911000660

DO - 10.1142/S1793536911000660

M3 - 期刊論文

AN - SCOPUS:80052612013

SN - 1793-5369

VL - 3

SP - 95

EP - 113

JO - Advances in Adaptive Data Analysis

JF - Advances in Adaptive Data Analysis

IS - 1-2

ER -