Dimensionality Reduction for Entity Specialization
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Solution Overview
Problem
Interpreting large datasets to determine meaningful insights about clients or service providers is challenging due to the numerous features they contain, making it difficult for businesses to tailor services effectively or for customers to select appropriate service providers.
Innovation Solution
A computer-implemented method that reduces the dimensionality of numerical representations of entities using techniques like principal component analysis and Uniform Manifold Approximation and Projection, allowing for the determination of entity specializations and providing visual representations to facilitate interpretation and recommendation of suitable service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If dimensionality reduction techniques are applied to entity data, then interpretability and ease of operation are improved, but information loss may occur
Solution Approach 1:
The patent extracts and retains only the most significant features from high-dimensional entity data through dimensionality reduction techniques. By identifying and preserving the most important dimensions that capture essential entity characteristics, the system achieves improved interpretability while minimizing information loss about the underlying entity structures.
Solution Approach 2:
The patent transforms entity representations by changing their dimensional parameters from high-dimensional vectors to reduced-dimensional representations. This parameter transformation maintains the essential informational content while presenting it in a more interpretable format that facilitates easier operation and analysis of entity characteristics.
2Measurement precision
If specialized entity representations are created for each entity type, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of entity analysis into distinct processing stages: initial high-dimensional representation creation, dimensionality reduction, specialization determination, and matching. By dividing the processing into these sequential segments, the system achieves precise entity specialization measurement while managing overall system complexity through modular organization of processing steps.
Solution Approach 2:
The patent resolves complexity by transitioning from high-dimensional entity representations to reduced-dimensional representations. This dimensional change simplifies the data structure while preserving the essential information needed for precise specialization determination, thereby reducing processing complexity without sacrificing measurement precision.
Data Source
AI summary
A computer-implemented method for determining entity characteristics. The method comprising: determining a numerical representation of each of a plurality of second entities, each numerical representation comprising a multi-dimensional vector characteristic of the respective second entity, wherein each second entity is associated with one or more first entities; reducing the dimensionality of the numerical representations to produce a plurality of reduced dimensionality numerical representations; and determining a specialisation of each of the plurality of first entities based on the respective reduced dimensionality numerical representations.