AI Reconstruction for High-Dimensional Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning methods struggle to detect outliers in high-dimensional data effectively, especially when the data distribution changes, as they are inadequate for handling complex data and adapting to distribution shifts.
Innovation Solution
A method and system that utilize an artificial intelligence generation model, such as an encoder-decoder neural network, to reconstruct original data and calculate an anomaly score based on the comparison between original and reconstructed data, thereby detecting abnormal target objects in high-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional distance- or density-based machine learning methods are used, then the detection process is simple, but the detection accuracy deteriorates when data dimensionality increases
Solution Approach 1:
The patent introduces an intermediary transformation process that maps high-dimensional data to a lower-dimensional latent space using learned transformations. This intermediary step preserves essential data characteristics while eliminating the curse of dimensionality, allowing accurate outlier detection through reconstruction error measurement rather than direct distance calculation in high-dimensional space.
Solution Approach 2:
The patent transforms the problem from detecting outliers in the original high-dimensional space to detecting them in a reconstructed space. By changing the dimensional perspective through learned transformations and reconstruction, the system achieves accurate outlier detection without being constrained by high-dimensional distance metrics.
2Device complexity
If conventional machine learning methods are used, then the model structure is simple, but the adaptability to distribution changes deteriorates
Solution Approach 1:
The patent employs dynamic adaptive mechanisms where the system automatically adjusts its transformation parameters and reconstruction processes based on the input data characteristics. The learned transformations adapt to different data distributions, enabling the model to maintain high detection accuracy across varying conditions without requiring manual reconfiguration.
Solution Approach 2:
The patent utilizes parameter changes in the learned transformation models to adapt to different data distributions. By learning optimal transformation parameters from the data, the system can adjust its behavior to match various distribution characteristics, achieving robust adaptability while maintaining a relatively simple overall architecture.
3Ease of operation
If distance- or density-based methods are used, then the computational approach is straightforward, but the handling of complex high-dimensional data becomes inadequate
Solution Approach 1:
The patent replaces traditional mechanical distance-based outlier detection mechanisms with a learned transformation and reconstruction system. Instead of relying on fixed geometric distance calculations, the system uses adaptive learned transformations that capture complex data structures, achieving reliable detection for complex high-dimensional data while maintaining computational efficiency through the learned model.
Data Source
AI summary
A method comprises obtaining original data representing one or more characteristics of a target object; selecting an artificial intelligence generation model from among a plurality of artificial intelligence generation models based on the original data; generating reconstructed data based on the original data using the selected artificial intelligence generation model; calculating an anomaly score by comparing the original data and the reconstructed data; and outputting detection result data indicating whether the target object is abnormal based on the anomaly score.