Difference between revisions of "Anomaly Detection"
m (→Principal Component Analysis (PCA) Anomaly Detection) |
m |
||
| Line 26: | Line 26: | ||
* [http://arxiv.org/abs/1906.03821 Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang] | * [http://arxiv.org/abs/1906.03821 Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang] | ||
______________________________________________________ | ______________________________________________________ | ||
| + | |||
| + | <b>Anomaly Detection</b>. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different. | ||
| + | |||
<youtube>LRqX5uO5StA</youtube> | <youtube>LRqX5uO5StA</youtube> | ||
| Line 39: | Line 42: | ||
<youtube>3swnsoydKTI</youtube> | <youtube>3swnsoydKTI</youtube> | ||
<youtube>B5mPqFOx09U</youtube> | <youtube>B5mPqFOx09U</youtube> | ||
| + | <youtube>LRqX5uO5StA</youtube> | ||
== [[Principal Component Analysis (PCA)]] Anomaly Detection == | == [[Principal Component Analysis (PCA)]] Anomaly Detection == | ||
| Line 49: | Line 53: | ||
http://doi.ieeecomputersociety.org/cms/Computer.org/dl/trans/tk/2013/07/figures/ttk20130714602.gif | http://doi.ieeecomputersociety.org/cms/Computer.org/dl/trans/tk/2013/07/figures/ttk20130714602.gif | ||
| − | + | <youtube>086OcT-5DYI</youtube> | |
<youtube>ExoAbXPJ7NQ</youtube> | <youtube>ExoAbXPJ7NQ</youtube> | ||
<youtube>UEPFCp5WpIY</youtube> | <youtube>UEPFCp5WpIY</youtube> | ||
<youtube>6lc6Oz0k9WA</youtube> | <youtube>6lc6Oz0k9WA</youtube> | ||
Revision as of 15:26, 20 March 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Case Studies
- ...find outliers
- Capabilities
- Embedding: Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...find outliers
- Internet of Things (IoT)
- Screening; Passenger, Luggage, & Cargo
- Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang
______________________________________________________
Anomaly Detection. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different.
Principal Component Analysis (PCA) Anomaly Detection
PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar
