Probabilistic Generative Latent Variable Models for Weak Supervision Classification
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Solution Overview
Problem
In machine learning, training models on Big Data without labeled data or external knowledge bases poses challenges, requiring manual labeling by subject matter experts, which is time-consuming and inefficient.
Innovation Solution
The use of probabilistic generative latent variable models, such as Factor Analysis, Gaussian Process Latent Variable Models, and Variational Inference Factor Analysis, to perform weak supervision classification by receiving user-defined patterns and signals, extracting information, and generating labeled datasets through probabilistic latent variable model analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling by subject matter experts is used, then data quality and reliability are improved, but time consumption and productivity are worsened
Solution Approach 1:
The patent introduces weak supervision systems and probabilistic models as intermediary components between raw data and final labeled datasets. These intermediaries automatically generate pseudo-labels using multiple labeling functions and probabilistic inference, reducing direct reliance on manual expert labeling while maintaining data quality through ensemble methods and uncertainty quantification.
Solution Approach 2:
The system creates pseudo-labels that copy the essential classification information needed for training, generated automatically through probabilistic models rather than direct expert annotation. Multiple labeling functions generate candidate labels that are then synthesized into final pseudo-labels, replicating the value of manual labeling at scale.
2Measurement precision
If manual labeling is used to ensure accurate labels, then measurement precision is improved, but loss of time and productivity are worsened
Solution Approach 1:
The system applies multiple labeling functions and probabilistic models to generate labels, using more computational resources and methods than a single manual annotator would use, to achieve high accuracy without the time cost of multiple manual passes. The ensemble of labeling functions provides redundant verification that improves accuracy while maintaining efficiency.
Solution Approach 2:
The probabilistic models incorporate feedback from multiple labeling functions and uncertainty estimates to iteratively improve label quality. The system identifies low-confidence predictions and can target those for additional review or refinement, creating a feedback loop that improves accuracy efficiently.
3Productivity
If weak supervision with probabilistic models is used, then productivity is improved by reducing manual labeling, but measurement precision may be worsened compared to manual labeling
Solution Approach 1:
The patent merges multiple labeling functions and probabilistic inference results into unified pseudo-labels. By combining evidence from diverse sources (different labeling functions, models, and data points) through probabilistic frameworks, the system achieves accuracy comparable to manual labeling while maintaining high productivity through automation.
Solution Approach 2:
The labeling system uses composite approaches combining multiple modeling techniques, labeling functions, and probabilistic methods to create robust pseudo-labels. This composite strategy leverages the strengths of different approaches to achieve high accuracy that rivals manual labeling.
Data Source
AI summary
Systems and methods for weak supervision classification with probabilistic generative latent variable models are disclosed. A method for weak supervision classification with probabilistic generative latent variable models may include: (1) receiving, by a generative model computer program, a plurality of records from a database; (2) receiving, by the generative model computer program, a plurality of user-defined label functions; (3) labeling, by the generative model computer program, each of the plurality of records with each of the plurality of user-defined label functions; (4) representing, by the generative model computer program, the plurality of records that are labeled with the user-defined label functions in a matrix; (5) performing, by the generative model computer program, probabilistic latent variable model analysis on the matrix using a probabilistic generative latent variable model; and (6) outputting, by the generative model computer program, a labeled dataset for the plurality of records.