RT - Journal Article
T1 - â€‹Rank based Least-squares Independent Component Analysis
JF - srtc-jsri
YR - 2018
JO - srtc-jsri
VO - 14
IS - 2
UR - http://jsri.srtc.ac.ir/article-1-271-en.html
SP - 247
EP - 266
K1 - Copula
K1 - independent component analysis
K1 - squared-loss mutual information.
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.
LA eng
UL http://jsri.srtc.ac.ir/article-1-271-en.html
M3 10.29252/jsri.14.2.247
ER -