SGR: A New Kernel for Outlier Detection in Multi-variate Sensor Data - Seema Sharma
-40% koodiga BOOKS
Saadetis 12-18 tööpäeva jooksul
30-päevane tagastamisõigus
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 apply ... Täielik kirjeldus
Võib-olla meeldib sulle ka
TOP
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI
Eliezer Yudkowsky, Nate Soares
-40% koodiga BOOKS
Meie tarnija laos
TOP
Open Circuits: The Inner Beauty of Electronic Components
Eric Schlaepfer, Windell Oskay
-40% koodiga BOOKS
Meie tarnija laos
TOP
Get started with MicroPython on Raspberry Pi Pico: The Official Raspberry Pi Pico Guide
-40% koodiga BOOKS
Meie tarnija laos
TOP
We Are Not Machines: The Fight for the Future of Work
-40% koodiga BOOKS
Meie tarnija laos
TOP
Foundations of Flavor: The Noma Guide to Fermentation: Including Step-By-Step Information on Making and Cooking with: Koji, Kombuchas, Shoyus, Misos, Vinegars, Garums, Lacto-Ferments and black Fruits Vegetables
-40% koodiga BOOKS
Meie tarnija laos
TOP
Nucleo Boards Programming with the STM32CubeIDE: Hands-on in more than 50 projects
-40% koodiga BOOKS
Meie tarnija laos
TOP
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
-40% koodiga BOOKS
Meie tarnija laos
TOP
Black Holes: The Key to Understanding the Universe
-40% koodiga BOOKS
Meie tarnija laos
TOP
Flowers For Algernon: A Modern Literary Classic
-40% koodiga BOOKS
Meie tarnija laos
TOP
Human Compatible: Artificial Intelligence and the Problem of Control
-40% koodiga BOOKS
Meie tarnija laos
TOP
How To Read Water: Clues & Patterns from Puddles to the Sea
-40% koodiga BOOKS
Meie tarnija laos
TOP
Make: Electronics: Learning by Discovery: A hands-on primer for the new electronics enthusiast
-40% koodiga BOOKS
Meie tarnija laos
TOP
Mastering the Arduino Uno R4: Programming and Projects for the Minima and WiFi
-40% koodiga BOOKS
Meie tarnija laos
TOP
The Idea Factory: Bell Labs and the Great Age of American Innovation
-40% koodiga BOOKS
Meie tarnija laos
TOP
Fundamentals of Aerodynamics ISE
Christopher Cadou, John D. Anderson
-40% koodiga BOOKS
Meie tarnija laos
Kirjeldus
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.
Lisateave
| Autor | Seema Sharma |
|---|---|
| Kirjastaja | LAP LAMBERT Academic Publishing |
| Väljalaskeaasta | 2017 |
| Kaanetüüp | Pehme kaanega |
| EAN | 9786202022965 |