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Sparsity of Principal Component Analysis on Presence of Outliers
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Mousa Golalizadeh *   , Amir Razaghi |
| Tarbiat Modares University |
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Abstract: (3013 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. |
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| Keywords: Principal Components Analysis, Outliers, Interpretability of Components, Robust and Sparse Principal Components Analysis, Crime Data. |
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Full-Text [PDF 512 kb]
(1099 Downloads)
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Type of Study: Applicable |
Subject:
General
Received: 2019/02/17 | Accepted: 2019/10/22 | Published: 2019/10/22
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