Gaussian Mixture Model Feature Transformation Evaluation
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Solution Overview
Problem
Current Gaussian Mixture Model (GMM) based feature transformation methods require large amounts of training and testing data, leading to increased resource consumption and long processing times, especially in mobile environments, where memory and power efficiency are critical.
Innovation Solution
A method that eliminates the need for testing data by calculating a trace measurement of the GMM during the training phase to evaluate the quality of the transformation model, allowing for reduced resource consumption and efficient evaluation of transformation performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If large amounts of training and testing data are used for GMM based feature transformation, then transformation quality is improved, but resource consumption and processing time increase
Solution Approach 1:
The patent extracts the evaluation function from the traditional training-testing framework by introducing a trace measurement that can be computed during training. This allows the system to evaluate transformation quality without requiring separate testing data, thereby reducing processing time while maintaining transformation quality.
Solution Approach 2:
The trace measurement is computed during the training phase itself, performing the evaluation action preliminarily before the traditional testing stage. This preliminary evaluation allows for early detection of transformation quality issues and eliminates the need for time-consuming separate testing with large datasets.
2Manufacturing precision
If large amounts of training and testing data are used for GMM based feature transformation, then transformation quality is improved, but memory consumption increases
Solution Approach 1:
The patent extracts the evaluation capability from the testing data requirement by implementing a trace measurement that operates on training data only. This extraction eliminates the need to store and process separate testing datasets, thereby reducing memory consumption while preserving transformation quality assessment.
3Manufacturing precision
If large amounts of training and testing data are used for GMM based feature transformation, then transformation quality is improved, but power consumption increases
Solution Approach 1:
By performing the quality evaluation during the training phase through trace measurement computation, the system completes the assessment action preliminarily. This eliminates the need for a separate testing phase that would consume additional power, thereby reducing overall power consumption while maintaining transformation quality.
Data Source
AI summary
An apparatus for providing efficient evaluation of feature transformation includes a training module and a transformation module. The training module is configured to train a Gaussian mixture model (GMM) using training source data and training target data. The transformation module is in communication with the training module. The transformation module is configured to produce a conversion function in response to the training of the GMM. The training module is further configured to determine a quality of the conversion function prior to use of the conversion function by calculating a trace measurement of the GMM.