Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/11/1678 |
| _version_ | 1797465410889580544 |
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| author | Meric Yucel Serdar Bagis Ahmet Sertbas Mehmet Sarikaya Burak Berk Ustundag |
| author_facet | Meric Yucel Serdar Bagis Ahmet Sertbas Mehmet Sarikaya Burak Berk Ustundag |
| author_sort | Meric Yucel |
| collection | DOAJ |
| description | A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy–entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced—seconds versus hours—encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare. |
| first_indexed | 2024-03-09T18:21:03Z |
| format | Article |
| id | doaj.art-e07b9f144b284758937399677d09fb13 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T18:21:03Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e07b9f144b284758937399677d09fb132023-11-24T08:19:04ZengMDPI AGEntropy1099-43002022-11-012411167810.3390/e24111678Brain Inspired Cortical Coding Method for Fast Clustering and Codebook GenerationMeric Yucel0Serdar Bagis1Ahmet Sertbas2Mehmet Sarikaya3Burak Berk Ustundag4National Software Certification Research Center, Istanbul Technical University, Istanbul 34469, TurkeyComputer Engineering Department, Istanbul Technical University, Istanbul 34469, TurkeyComputer Engineering Department, Istanbul University-Cerrahpasa, Istanbul 34320, TurkeyMaterials Science & Engineering Department, University of Washington, Seattle, WA 98195, USAComputer Engineering Department, Istanbul Technical University, Istanbul 34469, TurkeyA major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy–entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced—seconds versus hours—encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.https://www.mdpi.com/1099-4300/24/11/1678clusteringcodebook generationcortical coding modeldistortionentropy maximizationexecution time |
| spellingShingle | Meric Yucel Serdar Bagis Ahmet Sertbas Mehmet Sarikaya Burak Berk Ustundag Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation Entropy clustering codebook generation cortical coding model distortion entropy maximization execution time |
| title | Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation |
| title_full | Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation |
| title_fullStr | Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation |
| title_full_unstemmed | Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation |
| title_short | Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation |
| title_sort | brain inspired cortical coding method for fast clustering and codebook generation |
| topic | clustering codebook generation cortical coding model distortion entropy maximization execution time |
| url | https://www.mdpi.com/1099-4300/24/11/1678 |
| work_keys_str_mv | AT mericyucel braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration AT serdarbagis braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration AT ahmetsertbas braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration AT mehmetsarikaya braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration AT burakberkustundag braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration |