TY - JOUR T1 - ​Rank based Least-squares Independent Component Analysis TT - تحلیل مؤلفه‌های مستقل کم‌ترین توان‌های دوم رتبه-مبنا JF - srtc-jsri JO - srtc-jsri VL - 14 IS - 2 UR - http://jsri.srtc.ac.ir/article-1-271-en.html Y1 - 2018 SP - 247 EP - 266 KW - Copula KW - independent component analysis KW - squared-loss mutual information. 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. M3 10.29252/jsri.14.2.247 ER -