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Learning From PU Data Using Disentangled Representations
Omar Zamzam1, Haleh Akrami1, Mahdi Soltanolkotabi1
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Proceedings. International Conference on Image Processing
|October 9, 2025
Summary
This study introduces a novel neural network approach for Positive Unlabeled (PU) learning, effectively separating unlabeled data into positive and negative clusters. This method improves high-dimensional data classification accuracy compared to existing techniques.
Area of Science:
- Machine Learning
- Data Science
- Artificial Intelligence
Background:
- Classical Positive Unlabeled (PU) learning methods struggle with high-dimensional data complexity.
- Existing high-dimensional PU learning techniques are also affected by data complexity.
Purpose of the Study:
- To develop a robust PU learning method for high-dimensional data.
- To enhance the efficacy of clustering techniques in complex datasets.
- To improve the identification of positive and negative classes in partially labeled data.
Main Methods:
- Utilized a neural network to learn a data representation.
- Employed a novel loss function to project unlabeled data into distinct positive and negative clusters.
- Implemented a vector quantization strategy to refine cluster separation.
Main Results:
- Demonstrated superior performance over state-of-the-art methods on benchmark PU datasets.
- Successfully projected unlabeled data into well-separated positive and negative clusters.
- Validated the effectiveness of the neural network-based representation learning.
Conclusions:
- The proposed cluster-based neural network approach effectively addresses high-dimensional PU learning challenges.
- The method offers a simplified approach to PU learning, akin to low-dimensional settings.
- Theoretical justification supports the cluster-based strategy and algorithmic choices for enhanced classification.
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