Robust Feature Selection via Neighborhood Component Analysis
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Solution Overview
Problem
Existing machine learning models face inefficiencies in feature selection, leading to increased computational resources and reduced predictive accuracy due to the inclusion of irrelevant features, which are not effectively addressed by current techniques.
Innovation Solution
The implementation of robust feature selection using neighborhood component analysis, which determines weights for potential features through an objective function with a robust loss function and regularization term, allowing for the identification of relevant features and reduction of dimensionality, thereby optimizing predictive accuracy and reducing computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature selection techniques are used, then computational resources are reduced, but predictive accuracy deteriorates due to inclusion of irrelevant features
Solution Approach 1:
The patent replaces traditional mechanical feature selection methods with a machine learning-based neighborhood component analysis system that automatically evaluates and selects features based on their predictive power, thereby improving accuracy without proportionally increasing computational burden
Solution Approach 2:
The system changes the parameter of feature selection from manual or simple statistical methods to a learned parameter space using neighborhood component analysis, where features are selected based on optimized weights that balance predictive accuracy and computational efficiency
2Measurement precision
If all potential features are included in model training, then predictive accuracy may be improved, but computational burden increases
Solution Approach 1:
The patent extracts and selects only the most relevant features from the complete feature set using neighborhood component analysis, removing irrelevant features that would otherwise increase computational burden without contributing to predictive accuracy
Solution Approach 2:
The system applies partial action by selecting a subset of features rather than processing all available features, achieving sufficient predictive accuracy with reduced computational effort through the feature selection mechanism
3Quantity of substance
If irrelevant features are included in the model, then dimensionality is maintained, but predictive accuracy reduces
Solution Approach 1:
The patent converts the harmful effect of having too many features (including irrelevant ones) into a benefit by using the complete feature set as input for the neighborhood component analysis, which then identifies and selects only the beneficial relevant features while discarding irrelevant ones
Data Source
AI summary
A device may generate an objective function for determining weights for potential features corresponding to training data. The objective function may be generated using a robust loss function such that the objective function is at least continuously twice differentiable. The objective function may comprise a neighborhood component analysis objective function that includes the robust loss function. The device may determine the weights for the potential features using the objective function. The determining may comprise optimizing a value of the objective function for each potential feature. The weights may represent predictive powers of corresponding potential features. The device may provide the weights for the potential features.