Machine Learning Parameter Space Optimization via Dimensionality Reduction
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Solution Overview
Problem
Systems with multiple parameters face the challenge of combinatorial explosion, where the rapid growth of possible combinations exceeds available resources, making it impractical to test every unique combination to find optimal parameter values for achieving a key performance indicator (KPI).
Innovation Solution
A method using machine learning to perform dimensionality reduction and cluster analysis, decomposing the parameter space into single dimension functions, allowing for the identification of optimal parameter value combinations without testing every possible combination, thereby minimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every possible parameter combination is tested to determine optimal values, then measurement precision and reliability are improved, but resource consumption and time requirements increase exponentially due to combinatorial explosion
Solution Approach 1:
The patent segments the multi-dimensional parameter space into multiple one-dimensional subspaces, each corresponding to a single parameter. By independently analyzing and optimizing each parameter dimension separately rather than testing all combinations, the system avoids combinatorial explosion while maintaining optimization accuracy.
Solution Approach 2:
The patent transforms the problem from a multi-dimensional combinatorial search space into multiple one-dimensional optimization problems. This dimensional reduction allows the system to analyze parameter effects independently along each axis, converting an exponentially complex problem into a linearly scalable solution.
2Manufacturing precision
If every possible parameter combination is tested to determine optimal values, then manufacturing precision is improved, but device complexity and resource requirements increase exponentially
Solution Approach 1:
The testing system is segmented into independent one-dimensional analysis modules, each handling a single parameter. This segmentation simplifies the overall system complexity while maintaining the ability to precisely determine optimal parameter values through independent dimensional analysis.
Solution Approach 2:
The system reduces complexity by changing from a multi-dimensional combinatorial testing approach to multiple one-dimensional optimization approaches. This dimensional transformation simplifies the testing architecture while preserving optimization precision.
3Productivity
If comprehensive parameter testing is performed to achieve optimal KPI, then productivity is improved through better optimization, but resource consumption exceeds available capacity
Solution Approach 1:
The patent segments resource consumption across multiple independent one-dimensional analyses rather than concentrating resources on exhaustive multi-dimensional testing. This segmentation enables optimal KPI achievement with distributed, manageable resource allocation.
Solution Approach 2:
By transforming the optimization problem into one-dimensional subspaces, the system reduces resource requirements from exponential to linear scaling, enabling comprehensive parameter optimization within available resource constraints.
Data Source
AI summary
A method of machine learning includes performing dimensionality reduction on a parameter space by performing initial tests to determine scores for a plurality of parameter values in the parameter space, determining aggregate scores for a plurality of parameter value combinations, determining a ranking of the plurality of parameter value combinations based on the aggregate scores, and performing cluster analysis on the plurality of parameter value combinations to determine a set having highest aggregate scores. The method further includes performing additional tests, wherein each additional test is for a parameter value combination in the set. For each such parameter value combination, a probability of achieving a key performance indicator (KPI) is computed. Cluster analysis is then performed to determine a first subset of the set having highest probabilities of achieving the KPI. An operation is then performed on the first subset.