Latent Variable Classification for Feature Separation
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Solution Overview
Problem
Existing learning methods face challenges in clearly separating data into features, leading to potential loss of information during class classification, which can result in incomplete feature detection and data separation.
Innovation Solution
A learning device and method that classify latent variables into label and non-label features, using decoder parameters to generate reconstruction data and optimize parameters to minimize classification and reconstruction errors, ensuring clear separation of data features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If label features are input into the neural network for class classification, then classification task is solved, but information other than the class may disappear
Solution Approach 1:
The patent segments the feature extraction process into two independent neural networks: one dedicated to extracting label features for classification, and another for extracting non-label features. This segmentation allows each network to specialize in its specific function without the other interfering, thereby preserving all feature information while achieving classification goals.
Solution Approach 2:
The patent introduces an intermediary reconstruction process that takes the extracted features and attempts to reconstruct the original input data. This intermediary step serves as a verification mechanism to ensure that no critical information was lost during the feature extraction and classification process.
2Productivity
If features are processed for classification, then classification is achieved, but data separation into features becomes unclear
Solution Approach 1:
The patent employs separate neural networks for extracting label features and non-label features, clearly segmenting the feature extraction process. This segmentation ensures that each type of feature is processed independently and clearly, improving both classification efficiency and feature separation clarity.
Solution Approach 2:
The patent uses different reconstruction processes for label features and non-label features, analogous to using different colors to distinguish different types of features. This differentiation makes the feature separation explicit and clear, allowing the system to maintain high productivity while achieving precise feature separation.
Data Source
AI summary
According to an aspect of the present invention, there is provided a learning device including: a classification unit that classifies latent variables obtained from learning data used for learning into a label feature quantity and a non-label feature quantity; a decoding unit that decodes the label feature quantity and the non-label feature quantity classified by the classification unit by using decoder parameters to generate reconstruction data; and an optimization unit that optimizes the decoder parameters to minimize a classification error between the label feature quantity and label information used for classification by using the label feature quantity, and minimize a reconstruction error by using the label feature quantity and the non-label feature quantity.