Adaptive Product Quantization for Feature Vector Compression
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Solution Overview
Problem
Conventional product quantization methods for feature vectors suffer from inefficiencies due to fixed division numbers and cluster counts, leading to biased dispersion among sub-vectors and sub-optimal quantization efficiency.
Innovation Solution
The method generates dimension-variable sub-vectors and uses X-means clustering to automatically determine the optimal cluster number for each sub-vector group, improving quantization efficiency by allowing adaptive division and clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If feature vectors are evenly divided into sub-vectors with the same number of dimensions, then the division process is simple and systematic, but the degree of dispersion among different sub-vectors becomes biased and unequal
Solution Approach 1:
The patent applies local quality by allowing different sub-vector groups to have different numbers of dimensions based on their specific dispersion characteristics. Instead of uniformly dividing all feature vectors into equal-dimensional sub-vectors, the method dynamically determines the dimensionality of each sub-vector group according to its local data distribution, thereby achieving equal dispersion across all sub-vector groups while maintaining systematic processing through automated dimension determination.
2Productivity
If the number of clusters is fixed in advance for each sub-vector group, then the codebook generation process is efficient and straightforward, but the cluster number becomes either too large or too small depending on the specific sub-vector characteristics
Solution Approach 1:
The patent implements dynamics by making the cluster number adaptive rather than fixed. The method dynamically determines the optimal cluster number for each sub-vector group based on its specific characteristics and dispersion properties. This dynamic adjustment ensures that each sub-vector group receives an appropriate number of clusters tailored to its needs, improving quantization efficiency while maintaining computational feasibility through automated determination algorithms.
3Ease of operation
If product quantization is performed using a codebook generated from evenly divided sub-vectors with fixed cluster numbers, then the processing workflow is simple and consistent, but the quantization efficiency declines due to biased dispersion and inappropriate cluster numbers
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting key parameters including the number of dimensions for each sub-vector group and the cluster number for each group. Instead of using fixed parameters throughout the quantization process, the method modifies these parameters based on the actual data characteristics and dispersion patterns observed in different sub-vector groups, thereby achieving high quantization efficiency while maintaining a systematic workflow through automated parameter determination.
Data Source
AI summary
A data processing device according to an embodiment includes a sub-vector group generating unit, a codebook generating unit, and a converting unit. The sub-vector group generating unit generates, from a feature vector set of N number of D-dimensional feature vectors, M number of sub-vector groups (where M<D holds true). Each of the M number of sub-vector groups includes N number of dimension-variable sub-vectors obtained from the N number of D-dimensional feature vectors. For each of the M number of sub-vector groups, the codebook generating unit performs clustering of the N number of dimension-variable sub-vectors, and generates a codebook in which the representative vector of each cluster is associated with an index. The converting unit performs product quantization using the codebook and converts each of the N number of D-dimensional feature vectors into a compressed code made of a combination of M number of indexes.