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:: Volume 24, Issue 1 (9-2019) ::
Andishe 2019, 24(1): 117-128 Back to browse issues page
Sparsity of Principal Component Analysis on Presence of Outliers
Mousa Golalizadeh Dr. *, Amir Razaghi Mr.
Tarbiat Modares University
Abstract:   (647 Views)
‎The Principal Components Analysis is one of the popular exploratory approaches to reduce the dimension and to describe the main source of variation among data‎. ‎Despite many benefits‎, ‎it is encountered with some problems in multivariate analysis‎. ‎Having outliers among data significantly influences the results of this method and it sounds a robust version of PCA is beneficial  in this case‎. ‎In addition‎, ‎having moderate loadings in the final results makes the interpretation of principal components rather difficult‎. ‎One can consider a version of sparse components in this case‎. ‎We study a hybrid approach consisting of joint robust and sparse components and conduct some simulations to evaluate and compare it with other traditional methods‎. ‎The proposed technique is implemented in a real-life example dealing with the crime rate in the USA‎.
Keywords: ‎Principal Components Analysis‎, ‎Outliers‎, ‎Interpretability of Components‎, ‎Robust and Sparse Principal Components Analysis‎, ‎Crime Data.
Full-Text [PDF 512 kb]   (130 Downloads)    
Type of Study: Applicable | Subject: General
Received: 2019/02/17 | Accepted: 2019/10/22 | Published: 2019/10/22
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Golalizadeh M, Razaghi A. Sparsity of Principal Component Analysis on Presence of Outliers. Andishe. 2019; 24 (1) :117-128
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Volume 24, Issue 1 (9-2019) Back to browse issues page
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