:: Volume 14, Issue 2 (3-2018) ::
JSRI 2018, 14(2): 247-266 Back to browse issues page
​Rank based Least-squares Independent Component Analysis
Jafar Rahmani Shamsi , Ali Dolati *
, adolati@yazd.ac.ir
Abstract:   (531 Views)
 
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.
 
Keywords: Copula, independent component analysis, squared-loss mutual information.
Full-Text [PDF 349 kb]   (109 Downloads)    
Type of Study: Research | Subject: General
Received: 2017/02/22 | Accepted: 2018/02/7 | Published: 2018/03/17
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