An Unsupervised Hierarchical Clustering Approach to Improve Hopfield Retrieval Accuracy
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Joint Conference on Neural Networks (IJCNN), 2023, 2023-June
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| An_Unsupervised_Hierarchical_Clustering_Approach_to_Improve_Hopfield_Retrieval_Accuracy.pdf | Accepted version | 4.38 MB |
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Despite its efficiency the classical Hopfield network was a highly impractical data searching solution due to its limited storage capacity While the recently released modern Hopfield variant has increased its storage capacity its searching ability is heavily affected by local minima and saddle points which prevented it from becoming a worthy successor of the classical Hopfield network We propose a novel unsupervised clustering approach to bypass local minima and saddle points to enhance the overall robustness of the Hopfield network Our experimental results on benchmark MNIST indicate that our algorithm can increase the retrieval accuracy by over 20 in general against the Hopfield Update Rule proving that it is a far superior modelling solution
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