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Research Paper | Computer Science & Engineering | India | Volume 3 Issue 12, December 2014
An Evaluation of Projection Based Multiplicative Data Perturbation for KNN Classification
Bhupendra Kumar Pandya | Umesh Kumar Singh | Keerti Dixit
Abstract: Random projections have recently emerged as a powerful method for dimensionality reduction. In random projection (RP), the original high-dimensional data is projected onto a lower-dimensional subspace using a random matrix whose columns have unit lengths. In this method the data is projected on to a random subspace, which preserves the approximate Euclidean distances between all pairs of points after the projection. In this research paper we give experimental results on using RP as a dimensionality reduction tool and analysis Projection Based Multiplicative data perturbation for KNN Classification as a tool for privacy-preserving data mining.
Keywords: Random Projection, KNN Classification
Edition: Volume 3 Issue 12, December 2014,
Pages: 681 - 684
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