Hyperdimensional Representation for Adaptive Information Association and Memorization

Many computer vision applications rely on interpretable machine learning algorithms to analyze the data collected from various sources. We leverage Hyperdimensional Computing (HDC) as an innovative computational model that mimics key brain functionalities to achieve efficient and robust cognitive learning. We propose HDlm, a novel HDC-based cognitive representation capable of adaptive information association and memorization. HDlm first theoretically expands the HDC mathematics to support selective information association and adaptive memorization. Then, it exploits the proposed operations to support cognitive operations, including set membership, information retrieval, and item comparison. We evaluated our solution for a selection of applications related to visual data representation and sequence matching analysis. Our evaluation shows that HDlm provides more adaptive similarity metrics between objects that lead to better task performance.