Tri-Point Arbitration for Objective Similarity Analysis
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Solution Overview
Problem
Traditional two-point distance-based similarity analysis in machine learning is subjective and struggles to effectively combine attributes of different types, leading to non-unique outcomes and potential biases in data point selection.
Innovation Solution
The introduction of tri-point arbitration, where an arbiter data point represents the data set, calculates similarity metrics using distances between three points, reducing analyst subjectivity and improving attribute combination analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional two-point distance-based similarity analysis is used, then the analysis process is simple, but the results are subjective and non-unique due to analyst-dependent threshold selection
Solution Approach 1:
The patent introduces a third data point as an arbiter or mediator between two compared data points. This arbiter point objectively determines similarity by comparing distances from both points to the arbiter, eliminating the need for subjective analyst threshold selection. The arbiter acts as an intermediary that provides a reference frame for objective comparison.
2Device complexity
If traditional two-point distance analysis is used, then the computational complexity is low, but the ability to combine attributes of different types is limited
Solution Approach 1:
The patent transitions from two-point distance measurement to tri-point distance analysis, adding a third dimension (the arbiter point) to the comparison framework. This additional dimension enables the system to handle and combine attributes of different types more effectively by providing a reference point that can mediate between heterogeneous attributes.
3Ease of operation
If analyst-determined thresholds are used for similarity determination, then the process is straightforward, but biases in data point selection occur
Solution Approach 1:
The system uses the data itself (through the arbiter point selected from the data set) to determine similarity thresholds, rather than relying on external analyst input. The arbiter point, being part of the data set, provides an internal reference that is neutral and unbiased, allowing the data to speak for itself without external interference.
Data Source
AI summary
Systems, methods, and other embodiments associated with similarity analysis using tri-point arbitration are described. In one embodiment, a method includes selecting a data point pair and an arbiter point from a data set. A tri-point arbitration coefficient (ρTAC) is calculated for data point pairs based, at least in part, on a distance between the first and second data points and the arbiter point. A similarity metric is determined for the data set based, at least in part, on an aggregation of tri-point arbitration coefficients for data point pairs in the set of data points using the selected arbiter point.