Mixture Model Adaptation for Reliable Probability Data Transfer
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Solution Overview
Problem
The challenge lies in reliably transferring data while improving the accuracy of mixture models used to represent probability distribution data, as increasing the number of mixed distributions leads to increased data volume and potential transfer failures.
Innovation Solution
An information processing apparatus that acquires communication conditions, determines the optimal number of distributions for approximation based on these conditions, and generates a mixture model to balance accuracy and data transfer efficiency by adjusting the number of Gaussian distributions in the mixture model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of mixed distributions in the mixture model is increased to improve approximation accuracy, then the accuracy of the probability distribution data is improved, but the data volume increases leading to transfer failures
Solution Approach 1:
The patent dynamically adjusts the mixture number based on communication conditions. The determination unit selects an appropriate mixture number from a plurality of candidates according to the current communication condition, making the system adaptable rather than static. This resolves the contradiction by allowing the accuracy to be optimized without permanently increasing data volume beyond transfer capabilities.
Solution Approach 2:
The patent changes the parameter (mixture number) of the mixture model based on communication conditions. By selecting different mixture numbers from a predetermined set according to communication quality, the system optimizes the balance between approximation accuracy and data transfer reliability without requiring a fixed high-accuracy model that would always exceed transfer limits.
2Measurement precision
If the mixture number is increased to improve model accuracy, then the approximation quality is improved, but the reliability of data transfer deteriorates
Solution Approach 1:
The system dynamically adapts the mixture number based on real-time communication conditions. When communication quality is good, a higher mixture number can be used for better accuracy. When communication quality degrades, the mixture number is reduced to ensure reliable transfer, thus maintaining reliability while optimizing accuracy when possible.
Solution Approach 2:
The patent changes the mixture number parameter according to communication conditions to maintain transfer reliability. By selecting from multiple predetermined mixture numbers based on communication quality metrics, the system ensures that data transfer remains reliable while achieving the highest possible approximation accuracy under current conditions.
3Measurement precision
If a fixed high-accuracy mixture model is used, then the approximation accuracy is maintained, but the system cannot adapt to varying communication conditions
Solution Approach 1:
The patent implements a dynamic selection mechanism where the mixture number is chosen from multiple predetermined options based on communication conditions. This allows the system to adapt to varying network quality, device capabilities, and energy constraints while maintaining appropriate approximation accuracy for each scenario.
Solution Approach 2:
The system changes the mixture number parameter based on communication conditions including network quality, device state, and energy availability. This enables the system to maintain high accuracy when conditions permit while adapting to lower resource availability when conditions deteriorate, thus achieving both accuracy and adaptability.
Data Source
AI summary
An information processing apparatus 100 according to the present disclosure includes an acquisition unit 121 configured to acquire a communication condition of a communication route leading to a provision destination to which probability distribution data is provided, a determination unit 122 configured to determine a mixture number of a plurality of known distributions to be used when the probability distribution data is approximated based on the communication condition, and a generation unit 123 configured to generate a mixture model approximating the probability distribution data by mixing the distributions according to the determined mixture number.