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Privacy-preserved big data analysis based on asymmetric imputation kernels and multiside similarities
تجزیه و تحلیل داده های بزرگ حفظ شده با حفظ حریم خصوصی بر اساس هسته تقلبی نامتقارن و شباهت های چندگانه-2018 This study presents an efficient approach for incomplete data classification, where the entries of samples
are missing or masked due to privacy preservation. To deal with these incomplete data, a new kernel
function with asymmetric intrinsic mappings is proposed in this study. Such a new kernel uses three-side
similarities for kernel matrix formation. The similarity between a testing instance and a training sample
relies not only on their distance but also on the relation between the testing sample and the centroid of the
class, where the training sample belongs. This reduces biased estimation compared with typical methods
when only one training sample is used for kernel matrix formation. Furthermore, centroid generation
does not involve any clustering algorithms. The proposed kernel is capable of performing data imputation
by using class-dependent averages. This enhances Fisher Discriminant Ratios and data discriminability.
Experiments on two open databases were carried out for evaluating the proposed method. The result
indicated that the accuracy of the proposed method was higher than that of the baseline. These findings
thereby demonstrated the effectiveness of the proposed idea.
Keywords: Incomplete data analysis ، Partial similarity ، Multiside similarity ، Privacy preservation ، Kernel ridge Regression (KRR) ، Missing values ، Data imputation ، Kernel method ، Cloud computing ، Data analytics |
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