Principal Component Analysis for Test Data Generation
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Solution Overview
Problem
Conventional methods for generating test data and signals, such as those used in machine learning workflows and Arbitrary Waveform Generators, face challenges in accurately modeling high-dimensional data with variability, often resulting in inaccurate or resource-intensive processing due to treating measurements as independent or using complex mathematical interpolations.
Innovation Solution
The application of Principal Component Analysis (PCA) to transform and modify datasets, allowing for the extraction of insights from correlated variables and the generation of new data that accurately reflects original data relationships by mapping between different data domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods treat observed measurements as independent or use complex mathematical interpolations to generate test data, then the generated data may achieve certain accuracy, but the processing complexity and computational resources required increase significantly
Solution Approach 1:
The patent extracts the dominant patterns and correlations from training data by identifying principal components that capture the most significant variance. This extraction process separates the essential data characteristics from the complex raw measurements, enabling accurate test data generation with reduced processing complexity by working with fewer principal components rather than full-dimensional data
Solution Approach 2:
The patent transforms data from the original measurement space to a transformed space defined by principal components. This parameter transformation changes the representation of data from correlated raw measurements to independent principal component scores, simplifying the generation process while maintaining data accuracy through the mathematical properties of PCA
2Measurement precision
If conventional methods use complex mathematical interpolations and perturbations to model relationships between measurements, then data accuracy may be improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-computing the principal components and their loading matrices from training data before actual test data generation. This preprocessing step establishes the transformation framework in advance, so that subsequent test data generation only requires simple matrix multiplications and random sampling, dramatically reducing processing time compared to performing complex interpolations on the fly
3Adaptability or versatility
If conventional methods generate test data for high-dimensional datasets, then comprehensive test coverage is achieved, but the computational complexity and resources required increase significantly
Solution Approach 1:
The patent applies dimensionality change by projecting high-dimensional correlated measurements onto a lower-dimensional space of principal components. This dimensional transformation reduces the complexity of test data generation by working with fewer independent variables while preserving the essential variance and correlations through the mathematical properties of PCA, enabling comprehensive test coverage with reduced computational resources
Data Source
AI summary
A system includes an input for accepting a dataset including at least two sets of data in a dataset domain and one or more processors configured to derive at least two principal components from the dataset using principal component analysis, the at least two principal components being orthogonal to one another, map the dataset to a principal component domain derived from the at least two principal components, generate additional data in the principal component domain, and remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset. Methods of operation and description of storage media, the operation of which performs the above operations, are also described.