Geometric Data Labeling With Anchor Vectors for Unlabeled Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning techniques lack a cost-effective method for automating the labeling process of datasets without human intervention, especially when dealing with large unlabeled images from sources like social media platforms.
Innovation Solution
A computer-implemented method that uses semantically-named anchor vectors derived from source datasets to create labels for target data items based on geometric generalizations of distance, specifically utilizing Cayley-Menger content to output labels for target data items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to label large unlabeled images, then labeling accuracy can be maintained, but the cost and time consumption increase significantly
Solution Approach 1:
The system pre-computes anchor vectors from source datasets and establishes a geometric framework before labeling target data. This preliminary setup enables automated labeling without human intervention while maintaining consistency and accuracy through pre-defined semantic relationships and distance metrics.
Solution Approach 2:
The system creates semantic copies of data relationships by composing anchor vectors that represent source datasets. These vector compositions capture the essential semantic structure and can be applied to label target data items automatically, replacing manual labeling while preserving accuracy through geometric generalization.
2Extent of automation
If automated labeling is implemented without human intervention, then labeling cost is reduced, but there is no well-established theory for automating the labeling process
Solution Approach 1:
The system transforms the labeling problem into a geometric parameter space by computing distances and compositions of anchor vectors. By changing from semantic reasoning to geometric computation with defined metrics (Cayley-Menger content), the system achieves reliable automated labeling through mathematically grounded transformations rather than heuristic approaches.
Solution Approach 2:
Anchor vectors serve as intermediaries between source datasets and target data items. These composed vectors mediate the labeling process by encoding semantic relationships in a geometric form that can be systematically applied to automate labeling while maintaining reliability through consistent geometric transformations.
3Measurement precision
If geometric generalization with Cayley-Menger content is used, then automated labeling accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system segments the complex labeling task into distinct computational stages: computing anchor vectors from source datasets, composing these vectors to represent semantic relationships, calculating geometric distances using Cayley-Menger content, and finally generating labels based on distance rankings. This segmentation makes the complex geometric computations more manageable and systematic.
Solution Approach 2:
The system transitions from traditional semantic similarity measures to a geometric dimensionality framework using Cayley-Menger content. By embedding data relationships in a geometric space with defined distance metrics, the system achieves more precise automated labeling through dimensional transformation that captures complex relationships in a computable form.
Data Source
AI summary
An automated data labeling method, system, and computer program product that includes composing a semantically-named anchor vector derived from a source dataset into a sequence that defines a location description for target data items based on a generalization of distances into Cayley-Menger content and outputting a label for a target data item based on the location description.