Summary of Synthetic Data Generation and Automated Multidimensional Data Labeling For Ai/ml in General and Circular Coordinates, by Alice Williams and Boris Kovalerchuk
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates
by Alice Williams, Boris Kovalerchuk
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed SDG-ADL algorithm tackles the issue of insufficient training data for AI/ML models by integrating synthetic data generation and automated data labeling. The approach relies on General Line Coordinates (GLCs) to visualize multidimensional data losslessly. This enables the use of various GLC representations, such as Circular Coordinates, Parallel Coordinates, and Shifted Paired Coordinates, each highlighting unique data properties like interattribute distributions and outlier detection. The algorithm is implemented in computer software using the Dynamic Coordinates Visualization system (DCVis). Experimental results with real-world datasets demonstrate the impact on classifier performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence and machine learning models need lots of training data to work well. But sometimes, there just isn’t enough data available. This problem can make it hard to develop and use these models. To fix this issue, researchers have developed a new way to create fake data that looks like real data and label the data automatically. They’ve also created a special system called Dynamic Coordinates Visualization (DCVis) that helps them visualize complex data in multiple ways. By using this system, they can better understand how different parts of the data are connected and find patterns that might not be visible otherwise. This could help improve the performance of AI and ML models. |
Keywords
* Artificial intelligence * Data labeling * Machine learning * Outlier detection * Synthetic data
