Bioinspired Cortex-based Fast Codebook Generation

TL;DR

This paper introduces a bioinspired cortex-based algorithm for fast, efficient feature extraction from streaming data, outperforming traditional clustering methods in speed while maintaining accuracy, with broad potential applications.

Contribution

The paper presents a novel cortex-inspired feature extraction algorithm that converges to orthogonal features efficiently from streaming signals, improving computational speed and generalization.

Findings

Significantly reduced data processing time (seconds vs. hours).

Maintained encoding accuracy comparable to traditional methods.

Demonstrated superior clustering and vector quantization performance.

Abstract

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 sensory cortical networks in the brain. Dubbed as bioinspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in 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 cortex 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.