(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework
Background: Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
To scale outlier detection (OD) to large-scale, high-dimensional datasets, we propose TOD, a novel system that abstracts OD algorithms into basic tensor operations for efficient GPU acceleration.
The corresponding paper.
The code is being cleaned up and released. Please watch and star!
One reason to use it:
On average, TOD is 11 times faster than PyOD!
If you need another reason: it can handle much larger datasets:more than a million sample OD within an hour!
TOD is featured for:
- Unified APIs, detailed documentation, and examples for the easy use (under construction)
- Supports more than 10 different OD algorithms and more are being added
- TOD supports multi-GPU acceleration
- Advanced techniques like provable quantization
Programming Model Interface
Complex OD algorithms can be abstracted into common tensor operators.
For instance, ABOD and COPOD can be assembled by the basic tensor operators.
End-to-end Performance Comparison with PyOD
Overall, it is much (on avg. 11 times) faster than PyOD takes way less run time.
Code is being released. Watch and star for the latest news!
