DocumentCode :
177505
Title :
Extending multifractal analysis to negative regularity: P-exponents and P-leaders
Author :
Leonarduzzi, R. ; Wendt, Herwig ; Jaffard, S. ; Roux, S.G. ; Torres, M.E. ; Abry, Patrice
Author_Institution :
Univ. Nac. de Entre Rios, Entre Rios, Argentina
fYear :
2014
fDate :
4-9 May 2014
Firstpage :
305
Lastpage :
309
Abstract :
Scale invariance is a widely used concept to analyze real-world data from many different applications and multifractal analysis has become the standard corresponding signal processing tool. It characterizes data by describing globally and geometrically the fluctuations of local regularity, usually measured by means of the Hölder exponent. A major limitation of the current procedure is that it applies only to locally bounded functions or signals, i.e., to signals with positive regularity. The present contribution proposes to characterize local regularity with a new quantity, the p-exponent, that permits negative regularity in data, a widely observed property in real-world data. Relations to Hölder exponents are detailed and a corresponding p-leader multifractal formalism is devised and shown at work on synthetic multifractal processes, representative of a class of models often used in applications. We formulate a conjecture regarding the equivalence between Hölder and p-exponents for a subclass of processes. Even when Hölder and p-exponents coincide, the p-leader formalism is shown to achieve better estimation performance.
Keywords :
data analysis; estimation theory; fractals; signal processing; Holder exponent; data analysis; estimation performance; negative regularity; p-exponent; p-leader multifractal formalism; scale invariance; signal processing tool; synthetic multifractal analysis processing; Estimation; Fractals; Signal processing; Standards; Wavelet analysis; Wavelet transforms; estimation performance; multifractal analysis; negative local regularity exponent; scale invariance; wavelet Leaders;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
Type :
conf
DOI :
10.1109/ICASSP.2014.6853607
Filename :
6853607
Link To Document :
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