​Rank based Least-squares Independent Component Analysis

Jafar Rahmanishamsi, Ali Dolati
<span title="2018-03-01">2018</span> <i title="Armenian Green Publishing Co."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zgiunwux6nhrhdayw4vmtz4usm" style="color: black;">Journal of Statistical Research of Iran</a> </i> &nbsp;
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
more &raquo; ... ather 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.29252/jsri.14.2.247">doi:10.29252/jsri.14.2.247</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d5yi5m3s6jhkjfk3aonu7eti3i">fatcat:d5yi5m3s6jhkjfk3aonu7eti3i</a> </span>
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