TY - JOUR
JF - srtc-jsri
JO - JSRI
VL - 14
IS - 2
PY - 2018
Y1 - 2018/3/01
TI - Rank based Least-squares Independent Component Analysis
TT - تحلیل مؤلفههای مستقل کمترین توانهای دوم رتبه-مبنا
N2 - 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.
SP - 247
EP - 266
AU - Rahmani Shamsi, Jafar
AU - Dolati, Ali
AD -
KW - Copula
KW - independent component analysis
KW - squared-loss mutual information.
UR - http://jsri.srtc.ac.ir/article-1-271-en.html
DO - 10.29252/jsri.14.2.247
ER -