AU - Rahmani Shamsi, Jafar
AU - Dolati, Ali
TI - Rank based Least-squares Independent Component Analysis
PT - JOURNAL ARTICLE
TA - srtc-jsri
JN - srtc-jsri
VO - 14
VI - 2
IP - 2
4099 - http://jsri.srtc.ac.ir/article-1-271-en.html
4100 - http://jsri.srtc.ac.ir/article-1-271-en.pdf
SO - srtc-jsri 2
AB - In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of the proposed algorithm through simulation and real data analysis. Since the proposed algorithm uses rank values rather than the actual values of the observations, it is extremely robust to the outliers and suffers less from the presence of noise than the other algorithms.
CP - IRAN
IN -
LG - eng
PB - srtc-jsri
PG - 247
PT - Research
YR - 2018