Seema Sharma

SGR: A New Kernel for Outlier Detection in Multi-variate Sensor Data

• Broschiertes Buch

Produktbeschreibung

Approaches of identifying outliers based on prior knowledge of data distribution are generally inadequate. Density estimation using a kernel function is a non-parametric approach that does not require prior knowledge of the data distribution. The technique has been studied widely and several kernel density based outlier detection algorithms have been proposed. However, most of the work in the field is applying the technique on univariate data. In multivariate data, co-relation between different fields requires special techniques. Here, we present a new kernel for estimating the density in multivariate data space. Our kernel is more efficient to the known kernels as it has lowest MISE among the available kernel functions. Our algorithm employs Mahalanobis Distance as a metric. This ensures that the co-relation between the variables is accounted for while estimating the density. The proposed algorithm is a two step process in which density is computed in the first step. Based on the density estimates, Local Outlier Factor is assigned as measure of outlierness of the points. Finally, the points with an absolute Z-score of greater than or equal to three are classified as outliers.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Seitenzahl: 92
• Erscheinungstermin: 14. September 2017
• Englisch
• Abmessung: 220mm x 150mm x 6mm
• Gewicht: 155g
• ISBN-13: 9786202022965
• ISBN-10: 6202022965
• Artikelnr.: 49402729

Autorenporträt

Seema Sharma lavora come consulente nel settore dei media e del business in India. Ha conseguito un Master in Scienze della Comunicazione presso l'Università di Pune. La signora Seema si è anche dedicata alla scrittura freelance e alla pubblicazione di libri di narrativa basati su fatti reali. Questa ricerca è stata un sincero tentativo di far luce sulla questione della discriminazione di genere negli studi sui media.