Vector Quantization Encoding for Wide Energy Range Data
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Solution Overview
Problem
Conventional vector quantization methods struggle with approximating vectors with wide energy distributions, leading to low approximation accuracy and inefficient search processes in high-dimensional, large-scale data sets, especially when energy spans a wide range, and require exhaustive comparisons.
Innovation Solution
The proposed method employs a combination of Residual Vector Quantization (RVQ) and Recursive Normalized Vector Quantization (RNQ) to normalize and quantize input vectors, separating direction and energy for efficient encoding and indexing, using a mixture encoding scheme that simplifies the encoding process and improves search speed by approximating vectors through a combination of codewords and energy values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional vector quantization methods are used to compress high-dimensional vectors, then memory consumption is reduced and loading into memory becomes feasible, but approximation accuracy deteriorates when energy spans a wide range
Solution Approach 1:
The patent divides the vector quantization process into multiple stages (first-order, second-order, etc.), where each stage quantizes a portion of the vector. This segmentation allows the system to use fewer codewords per stage while maintaining overall approximation accuracy, thus reducing memory consumption without sacrificing precision when energy spans a wide range.
Solution Approach 2:
The patent transforms the single-stage quantization problem into a multi-stage hierarchical structure, adding a temporal dimension to the quantization process. By distributing the quantization task across multiple stages with progressively refined codewords, the system achieves better approximation accuracy with reduced memory requirements compared to conventional single-stage methods.
2Device complexity
If a limited number of codewords are used in quantization to reduce computational costs, then computational complexity is reduced, but the ability to approximate vectors with wide energy distributions deteriorates
Solution Approach 1:
The patent segments the quantization process into multiple orders or stages, where each stage uses a limited set of codewords. The first-order quantization handles coarse approximation with fewer codewords, while subsequent orders refine the approximation. This segmentation allows the system to maintain low computational complexity at each stage while achieving high overall approximation accuracy for vectors with wide energy distributions.
Solution Approach 2:
The patent performs preliminary coarse quantization in earlier stages to reduce the search space and computational burden for subsequent refinement stages. By pre-processing the vector through initial quantization with limited codewords, the system reduces computational complexity while preserving enough information to enable accurate final approximation.
3Measurement precision
If exhaustive comparison between query and encoded candidates is performed, then search accuracy is maintained, but search speed deteriorates and scalability to large-scale tasks is lost
Solution Approach 1:
The patent segments the search process into multiple stages corresponding to the multi-order quantization. Instead of performing exhaustive comparison with all candidates, the system first filters candidates using first-order quantization codes, then progressively refines the candidate set using second-order and higher quantization codes. This segmentation dramatically reduces the number of comparisons needed while maintaining search accuracy.
Solution Approach 2:
The patent performs preliminary filtering using coarser quantization codes before conducting detailed comparisons. By pre-processing the search through hierarchical filtering based on multi-order quantization codes, the system eliminates most non-matching candidates early in the process, maintaining high search accuracy while achieving scalable performance on large-scale tasks.
Data Source
AI summary
The present invention relates to a method for encoding a candidate vector for searching for a neighbor that is nearest to a query in a candidate dataset, the method comprising a normalization step of normalizing an input vector to obtain a direction vector and vector energy; a quantization step of quantizing the direction vector to obtain a code word and a residual vector; a step of repeating the normalization step and the quantization step, as many times as a predetermined number of encoding times, by using the residual vector as an input vector; and a step of encoding the candidate vector by using one or more code words and energy of one or more vectors resulting from the repetition. According to the present invention, a dataset having a very wide range of energy values can be effectively approximated and higher precision thereof can be obtained.