Title :
Generalize higher-order moments in independent component analysis
Author :
Coleman, Jeffrey O.
Author_Institution :
US Naval Res. Lab., Washington, DC, USA
Abstract :
In independent component analysis (ICA), random-variable independence is often equated with factorization of the joint moments, expectations of products of powers. This paper shows that many nonpower functions are equally useful: if E[f(X)g(Y)] factors into E[f(X)]E[g(Y)] for every f and g from an independence class, then random variables X and Y are independent. Examples of and sufficient conditions for independence classes are presented for bounded random variables
Keywords :
identification; random processes; set theory; signal processing; statistical analysis; Borel functions; Borel sets; bounded random variables; generalized higher-order moments; independence classes; independent component analysis; independent random variables; joint moments factorization; nonpower functions; products of powers expectations; random-variable independence; signal procesing; sufficient conditions; sufficient identification criteria; Bismuth; Independent component analysis; Random variables; Sufficient conditions;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
0-7803-6293-4
DOI :
10.1109/ICASSP.2000.861896
