Semi-Supervised Data Labeling via Iterative Distance Matrix
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Solution Overview
Problem
Supervised learning algorithms require extensive human labor and resources for labeling training data, making it complex and expensive to fully label entire datasets, while semi-supervised learning relies on a small labeled portion, which may not be sufficient for accurate predictions or classifications.
Innovation Solution
A semi-supervised data labeling system that initializes a classification matrix, computes a converged classification matrix, and updates target variable values based on maximum label probabilities, iteratively selecting observation vectors for labeling to minimize distance and improve prediction/classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning algorithms are used to improve prediction accuracy, then classification precision is improved, but human labor and labeling costs increase significantly
Solution Approach 1:
The system enables unlabeled data to contribute to model training through semi-supervised learning. The algorithm automatically propagates labels from labeled to unlabeled data points using the classification matrix and distance metrics, allowing the system to self-enhance without requiring manual labeling of all data points.
Solution Approach 2:
Instead of requiring complete labeling of the entire dataset, the system uses a small portion of labeled data (partial action) and leverages the semi-supervised learning algorithm to infer labels for the remaining unlabeled data, achieving near-supervised performance with minimal human labeling effort.
2Productivity
If only a small portion of training data is labeled to reduce human labor, then labeling costs decrease, but prediction accuracy deteriorates
Solution Approach 1:
The system computes a classification matrix that captures relationships between all data points (labeled and unlabeled). This matrix provides feedback about the structure and distribution of the data, enabling the algorithm to iteratively refine label predictions for unlabeled points based on their relationships to labeled points, thereby maintaining high accuracy despite limited initial labeling.
Solution Approach 2:
The system transitions from considering only labeled data in the traditional supervised learning framework to incorporating unlabeled data through an additional dimension - the classification matrix that encodes pairwise relationships. This dimensional expansion allows the system to utilize the full dataset structure, not just the labeled portion, to improve prediction accuracy.
3Reliability
If extensive data labeling is performed to improve model performance, then classification reliability is improved, but time and computational resources increase
Solution Approach 1:
The system performs preliminary computation of the classification matrix and distance metrics using only the small labeled portion of data. This preliminary action establishes the structural relationships in the data space, which are then used to efficiently infer labels for unlabeled points, avoiding the need for time-consuming manual labeling of the entire dataset while maintaining model reliability.
Data Source
AI summary
A computing device predicts occurrence of an event or classifies an object using semi-supervised data. A label set defines permissible values for a target variable. A value of the permissible values is defined for a subset of observation vectors. A predefined number of times, a distance matrix is computed that defines a distance value between pairs of observation vectors using a distance function and a converged classification matrix; a number of observation vectors is selected that have minimum values for the distance value; a label is requested and a response is received for each of the selected observation vectors; the value of the target variable is updated for each of the selected observation vectors with the received response; and the value of the target variable is determined again by recomputing the converged classification matrix. The value of the target variable for each observation vector is output to a second dataset.