Synthetic Training Data Generation via Nearest Neighbor Approximation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating additional training data for machine learning algorithms are complex and resource-intensive, making them difficult to implement with ordinary data processing systems, especially when dealing with large or high-dimensional data sets.
Innovation Solution
A method that approximates nearest neighbors for additional data points based on existing training data, using robust statistics to determine data values, thereby simplifying the generation of additional training data with reduced resource consumption, such as low storage and computing capacities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If known augmentation methods are used to generate additional training data, then the completeness of training data is improved, but the device complexity and resource consumption increase significantly
Solution Approach 1:
The patent creates additional training data by copying and transforming existing data points through simple mathematical operations (adding noise, scaling, rotating) rather than using complex augmentation methods. This allows generation of synthetic training data that maintains the statistical properties of the original data while requiring minimal computational resources.
Solution Approach 2:
The invention transforms existing data points by changing their parameters (adding random noise, scaling factors, or rotational transformations) to create varied training examples. This parameter transformation approach generates diverse training data without requiring complex processing, thereby improving data completeness while keeping the method simple and resource-efficient.
2Reliability
If known augmentation methods are used to generate additional training data, then the coverage of process states is improved, but the computing capacity requirements increase
Solution Approach 1:
The patent generates additional training data by copying existing data points and applying simple transformations rather than using computationally intensive augmentation techniques. This copying approach maintains the statistical characteristics of the original data while requiring minimal computing power, thus achieving broad coverage of process states without increasing hardware requirements.
Solution Approach 2:
The invention uses simple, lightweight data transformation operations (adding noise, scaling) that can be executed quickly and with minimal computational resources. These simple operations are sufficient to generate diverse training data that covers various process states, eliminating the need for expensive complex augmentation methods.
3Measurement precision
If all data points are considered to determine nearest neighbors, then the accuracy of nearest neighbor approximation is improved, but the computational time increases with square complexity
Solution Approach 1:
Instead of considering all data points to determine nearest neighbors, the patent uses a partial approach by limiting the number of neighbors to consider or using sampling methods. This partial action reduces the computational complexity from O(n²) to a more manageable level while still achieving sufficient accuracy for training the machine learning algorithm.
Solution Approach 2:
The invention performs preliminary organization of data (such as sorting or indexing) before determining nearest neighbors, which allows for faster query processing. By preparing the data structure in advance, the patent reduces the computational time required during nearest neighbor determination while maintaining approximation accuracy.
Data Source
AI summary
A method is for generating training data for training a machine learning algorithm. The training data respectively include a data point and a data value associated with the data point. The method includes providing first training data for training the machine learning algorithm, providing an additional data point, and approximating nearest neighbors of the additional data point based on the data points of the first training data. The method further includes determining a data value associated with the additional data point from data values associated with the nearest neighbors of the additional data point. A data pair, including the additional data point and the data value associated with the additional data point, forms additional training data.