An Evaluation of Projection Based Multiplicative Data Perturbation for KNN Classification
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
How to Cite?: Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit, "An Evaluation of Projection Based Multiplicative Data Perturbation for KNN Classification", Volume 3 Issue 12, December 2014, International Journal of Science and Research (IJSR), Pages: 681-684, https://www.ijsr.net/getabstract.php?paperid=25111418, DOI: https://dx.doi.org/10.21275/25111418