Hyper-parameter Optimization via Matrix Factorization

The patent replaces the mechanical trial-and-error experimentation process with a theoretical mathematical framework based on matrix factorization. Instead of iteratively testing hyper-parameters against validation sets, the system uses covariance matrix analysis and energy threshold calculations to directly determine optimal hyper-parameters, eliminating time-consuming guesswork while maintaining optimization accuracy.

The patent transforms the hyper-parameter optimization problem from an empirical search process into a mathematical parameter analysis problem. By changing the approach from validation-based experimentation to matrix factorization-based calculation, the system derives hyper-parameters through mathematical relationships involving covariance matrices and energy thresholds, significantly reducing the time required while preserving optimization precision.

The patent replaces unreliable heuristic rules with a rigorous mathematical system based on matrix factorization theory. The theoretical framework provides concrete, justifiable methods for determining hyper-parameters through covariance matrix analysis and energy threshold calculations, replacing guesswork-based rules of thumb with scientifically grounded procedures that are both reliable and theoretically sound.

The patent transforms arbitrary rule-of-thumb hyper-parameter selection into a systematic parameter analysis approach. By introducing mathematical parameters such as covariance matrices, energy thresholds, and basis weight values, the system provides reliable, theoretically-justified guidance for hyper-parameter selection, eliminating the unreliability of informal heuristics while maintaining ease of operation through structured calculations.

The patent replaces the mechanical rule of increasing feature maps to balance computational load with a theoretical matrix factorization approach. The system uses covariance matrix analysis and energy threshold calculations to objectively determine the optimal number of feature maps for each layer, providing reliable, mathematically-justified computational balancing that eliminates the unreliability of heuristic compensation methods.

The patent transforms the subjective rule of compensating for spatial dimension reduction into an objective parameter optimization problem. By introducing mathematical parameters (covariance matrices, energy thresholds, basis weight values) to analyze and determine feature map counts, the system achieves reliable computational load balancing based on theoretical calculations rather than unreliable heuristic adjustments.