Feature Representation Adjustment for Small-Sample Classification
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Solution Overview
Problem
Training machine learning models with a small number of samples is challenging due to the inability to accurately summarize true distribution characteristics, leading to inaccurate model training.
Innovation Solution
Acquire feature representations of samples, adjust them to concentrate same-class distributions and separate different-class distributions, and use these adjusted representations to train a classification model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of training samples are collected and annotated, then model training accuracy is improved, but cost increases
Solution Approach 1:
The patent changes the parameters of feature representations through adjustment operations, transforming them to better capture distribution characteristics. By adjusting feature representations rather than increasing sample quantity, the system achieves improved model accuracy with fewer samples, directly resolving the contradiction between accuracy and sample quantity.
Solution Approach 2:
The patent creates adjusted feature representations that copy and enhance the essential characteristics of original samples. Through feature adjustment, multiple synthesized feature representations are generated that preserve true distribution characteristics, allowing the model to learn from a smaller number of effective training samples.
2Quantity of substance
If a small number of training samples are used, then cost is reduced, but model training accuracy deteriorates
Solution Approach 1:
The patent applies parameter adjustment to feature representations to enhance their discriminative capability. By transforming feature parameters to better reflect true distribution characteristics, the system maximizes the information extraction from limited samples, achieving high accuracy despite the small sample size.
Solution Approach 2:
The patent introduces adjusted feature representations as an intermediary between original samples and the classification model. These adjusted features serve as a bridge that enhances the information content, allowing the model to accurately learn patterns from fewer samples by processing enhanced feature representations rather than raw samples directly.
3Measurement precision
If feature representations are adjusted to concentrate same-class distributions, then classification accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent implements parameter adjustment operations on feature representations to concentrate same-class distributions and separate different-class distributions. By systematically transforming feature parameters according to defined adjustment rules, the system achieves improved classification accuracy through enhanced feature separability while maintaining manageable processing complexity through structured adjustment mechanisms.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for analyzing samples. The method includes acquiring a set of feature representations associated with a set of samples. The set of samples illustratively have classification information for indicating classifications of the set of samples. The method further includes adjusting the set of feature representations so that distances between feature representations of samples corresponding to the same classification are less than a first distance threshold. The method further includes training a classification model based on the adjusted set of feature representations and the classification information. The classification model is illustratively configured to receive an input sample and determine a classification of the input sample. In this manner, a relatively accurate classification model can be trained using a small number of samples, thereby reducing computation time and required computation capacity.