Hybrid Vector Inner Product Computation via Component Segmentation
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Solution Overview
Problem
Current techniques for computing inner products of dense and sparse vectors are inefficient and inaccurate when applied to hybrid vectors, which have both dense and sparse components, leading to poor performance in data mining and machine learning tasks.
Innovation Solution
The system identifies and separates dense and sparse components in hybrid vectors, applying efficient techniques for each type to compute inner products separately and using residual reordering to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current techniques for computing inner products of dense vectors are applied to hybrid vectors, then dense component processing is efficient, but sparse component processing becomes inaccurate and inefficient
Solution Approach 1:
The patent segments hybrid vectors into dense components and sparse components, applying specialized inner product computation techniques to each component type. Dense components are processed using efficient dense vector algorithms, while sparse components use optimized sparse vector algorithms, thereby resolving the contradiction between processing efficiency and accuracy for hybrid vectors.
2Productivity
If current techniques for computing inner products of sparse vectors are applied to hybrid vectors, then sparse component processing is efficient, but dense component processing becomes inaccurate and inefficient
Solution Approach 1:
The patent divides hybrid vectors into distinct dense and sparse components, allowing each component to be processed by the most appropriate algorithm. This segmentation enables sparse components to be handled efficiently with sparse vector techniques while dense components receive dedicated dense vector processing, eliminating the accuracy and efficiency losses that occur when using sparse techniques on dense data.
3Device complexity
If a single inner product technique is used for both dense and sparse vectors, then implementation is simple, but performance degrades for hybrid vectors
Solution Approach 1:
The patent implements a segmented processing architecture where hybrid vectors are automatically decomposed into dense and sparse components that are then processed by specialized algorithms. This approach increases system complexity only marginally while delivering substantial performance improvements for hybrid vector processing, effectively resolving the contradiction between simplicity and efficiency.
4Device complexity
If conventional inner product methods are used on hybrid vectors, then implementation is straightforward, but computational feasibility deteriorates for large datasets
Solution Approach 1:
The patent applies segmentation by decomposing hybrid vectors into dense and sparse components and processing each with optimized algorithms appropriate to its characteristics. This reduces computational time significantly for large datasets containing hybrid vectors, while the added implementation complexity remains manageable through automated component identification and routing.
Data Source
AI summary
A systems and method for providing various improvements in the computing time and accuracy for finding items using a hybrid vector space inner-product search are described. In one example implementation, a computer system comprising: at least one processor; and a data store of hybrid records, each hybrid record in the data store including a dense component and a sparse component memory storing instructions that, when executed by the at least one processor, cause the system to: receive a query request that includes a query vector; generate a dense query component and a sparse query component from the query vector; determine dense component similarity values between the dense query component and dense components of at least some of the hybrid records; determine sparse component similarity values using the sparse query component and sparse components of at least some of the hybrid records; select an initial set of hybrid records from the data store based on the dense component similarity values and the sparse component similarity values; and respond to the query request with at least some of the records from the initial set of hybrid records, is described.