Vector Symbolic Architecture Lookup via P-Component Similarity Search

The patent segments the high-dimensional vector space into multiple lower-dimensional subspaces or clusters. Instead of performing brute force search across the entire space, the search is divided into multiple smaller search problems in each subspace, significantly reducing computational complexity while maintaining search completeness through aggregation of results from all subspaces.

The patent transforms the search problem from the original high-dimensional space into a different dimensional representation or projected space where similarity searches can be performed more efficiently. This dimensionality change allows the system to maintain search accuracy while reducing the computational burden of comparing all vector pairs.

The patent divides the high-dimensional vector representation into multiple segments or components, each handling different aspects of the data. This segmentation allows the system to maintain the representational power of high-dimensional vectors while reducing search complexity by operating on smaller vector segments independently and combining results.

The patent employs dimensionality reduction techniques or projects high-dimensional vectors into lower-dimensional spaces for search operations, then maps results back to the original high-dimensional space. This approach preserves the representational capacity needed for complex data while enabling efficient similarity searches in the reduced dimensional space.