Genetic Feature Selection via Noise Vector Filtering

The patent extracts and removes noise components from genetic feature data through statistical filtering. By separating signal from noise in the genetic data, the method reduces the effective dimensionality of the problem while preserving the meaningful associations between genetic features and traits, thereby reducing computational complexity without sacrificing detection accuracy.

The patent transforms the original genetic feature parameters by applying statistical operations and noise filtering. This parameter transformation converts the underdetermined problem with many features into a more manageable form where the effective number of independent parameters is reduced, allowing accurate association detection with lower computational complexity.

The patent introduces noise vectors as an intermediary element to model the relationship between genetic features and traits. By incorporating explicit noise terms in the statistical model, the method can properly account for the excess features and determine significant associations even when the number of genetic features exceeds the number of individuals, maintaining statistical rigor.

The patent replaces traditional mechanical statistical methods with a noise-filtering approach based on statistical signal processing. Instead of relying on conventional methods that struggle with p > n problems, the invention uses statistical operations to filter noise and identify significant associations, enabling accurate determination even with more features than samples.