Nearest Neighbor Search Using ECC Cluster Partitioning
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Solution Overview
Problem
Traditional techniques for finding nearest neighbors in large datasets, such as locality-sensitive hashing (LSH), require substantial computational resources and memory for index construction and maintenance, and suffer from poor recall, making them inefficient for scalable and high-dimensional datasets.
Innovation Solution
A system utilizing error correction codes, specifically polar codes, to partition the embedding space into clusters, reducing memory requirements and improving recall by determining codewords that indicate the nearest clusters to a query entry, allowing for efficient nearest neighbor searches with minimal indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If locality-sensitive hashing (LSH) is used for nearest neighbor search, then the search capability is provided, but substantial computational resources and memory are required for index construction and maintenance
Solution Approach 1:
The embedding space is partitioned into clusters using error correction codes, where each codeword represents a cluster center. This segmentation allows the dataset to be organized into multiple clusters, reducing the memory footprint compared to storing complete LSH indices while maintaining efficient search capability through cluster-based querying.
Solution Approach 2:
The patent extracts only the essential cluster center information (codewords) from the complete index structure, storing minimal index data that points to cluster centers rather than maintaining full LSH tables. This extraction reduces memory consumption while preserving the ability to perform nearest neighbor searches by querying cluster centers.
2Reliability
If locality-sensitive hashing (LSH) is used for nearest neighbor search, then the search functionality is achieved, but poor recall is observed
Solution Approach 1:
By segmenting the search space into multiple clusters defined by error correction code codewords, the system improves recall by distributing data points across multiple clusters. This segmentation allows queries to potentially access relevant neighbors across cluster boundaries, improving recall compared to single-table LSH while maintaining manageable index complexity through the structured codeword representation.
Solution Approach 2:
The patent changes the fundamental parameter of index representation from LSH hash tables to error correction code codewords. This parameter change enables the index to achieve better recall by leveraging the mathematical properties of polar codes, where the codeword structure inherently provides better coverage and recall characteristics without proportionally increasing complexity.
3Productivity
If traditional nearest neighbor search methods are used, then simplicity is maintained, but they do not scale well to very large datasets
Solution Approach 1:
The patent applies segmentation by dividing the large dataset into multiple clusters represented by codewords. This segmentation enables scalable processing of very large datasets by allowing parallel query processing across clusters and reducing the computational burden on any single processing unit, thereby improving scalability while managing computational resource usage efficiently.
Solution Approach 2:
The patent substitutes the traditional mechanical index structures (LSH tables) with an error correction code-based system. This substitution replaces the conventional approach with a mathematically-founded system that scales more efficiently to large datasets, reducing computational resources required for index construction and maintenance while improving scalability.
Data Source
AI summary
Low latency search for nearest neighbors in a dataset containing a large number of entries is improved using an error correction code (ECC) for partitioning data into clusters and retrieval. During initialization and preprocessing a d-dimensional space with clusters corresponding to ECC codewords is specified. Entries in the dataset are embedded into this space and associated with respective codewords, each codeword specifying a cluster. An index associates the codewords, clusters, and entries. During a query of the dataset, a query entry is processed to determine a query embedding in the d-dimensional space. The query embedding is used as input for a list decoder of the ECC. The list decoder provides a set of nearest codewords, with those codewords representing a set of candidate clusters that may contain nearest neighbors. The dataset entries associated with the candidate clusters may then be searched to determine query results comprising specific entries.