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Abstract

Background

There has been extensive research on low-rank representation methods, and we firmly believe that this approach holds tremendous potential in addressing image-related challenges.

Objective

Medical image databases.

Method

We present low-loss low-rank representation(LLRR) to build a low-loss low-rank classifier (LLC) and a low-loss Low-rank discriminant projection(LLDP).

Results

The LLC algorithm exhibits a disease classification accuracy that is superior, with a maximum difference of 10%, compared to other algorithms. After pairing the feature extractors with a Nearest Neighbor Classifier, LLDP achieved a maximum 5.6% advantage in disease classification accuracy. In other words, feature extraction and classification methods can be constructed based on the representation method proposed, and the performance of LLRR is superior.

Conclusion

The experimental results indicate that LLDP and LLC consistently outperform other state-of-the-art methods, thus establishing LLRR as an effective data representation method.

© 2025 The Author(s). Published by Bentham Science Publishers
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-01-21
2025-10-29

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/content/journals/eng/10.2174/0118722121349744250102163111
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Low-loss Low-rank Representation for Medical Image Analysis
Bentham Science Publishers ; https://doi.org/10.2174/0118722121349744250102163111
/content/journals/eng/10.2174/0118722121349744250102163111
/content/journals/eng/10.2174/0118722121349744250102163111
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  • Article Type:     Research Article
Keywords: medical image recognition ; feature extraction ; accuracy ; Low-rank representation ; image representation ; discriminative projection ; classification

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