Using data images for outlier detection
| Autor: | Jeffrey L. Solka, David J. Marchette |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Statistics and Probability
Color histogram business.industry Applied Mathematics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Visualization Data set Computational Mathematics Random variate Data visualization Computational Theory and Mathematics Computer Science::Computer Vision and Pattern Recognition Outlier Anomaly detection Artificial intelligence business Row Mathematics |
| Zdroj: | Computational Statistics & Data Analysis. 43:541-552 |
| ISSN: | 0167-9473 |
| DOI: | 10.1016/s0167-9473(02)00291-8 |
| Popis: | The data image has been proposed as a method for visualizing high-dimensional data. The idea is to map the data into an image, by using gray-scale (or color) values to indicate the magnitude of each variate. Thus, the image for a data set of size n and dimension d is a d × η image, where the columns correspond to observations and the rows to variates. We consider the application of this idea to the detection of outliers, providing a simple visualization technique that highlights outliers and clusters within the data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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