Minimal Vector Labeling for Machine Learning Clustering
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Solution Overview
Problem
Supervised learning models require large volumes of labeled data for accurate multiclass classification, which is tedious and time-consuming, especially in scenarios with class imbalance, leading to inaccurate predictive performance.
Innovation Solution
The system employs a homomorphic dimensionality reduction algorithm and centroid-based clustering to identify optimal clusters, selecting representative vectors for labeling, and uses inductive learning and inductive forgetting to iteratively improve the machine learning model without extensive manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning models are trained with large volumes of labeled data to achieve accurate multiclass classification, then prediction accuracy is improved, but the time and effort required for manual labeling increases significantly
Solution Approach 1:
The patent segments the large dataset into multiple clusters using centroid-based clustering algorithms. By dividing the data into manageable clusters, the system can select representative vectors from each cluster for labeling, rather than labeling all data points. This segmentation approach maintains prediction accuracy while significantly reducing the volume of data requiring manual labeling.
Solution Approach 2:
The patent performs preliminary clustering and selection of representative vectors before the actual labeling process. By pre-identifying which data points are most representative of each cluster, the system prepares the optimal subset for labeling in advance, reducing the overall time required for the labeling process while ensuring accuracy.
2Measurement precision
If manually labeled data is used for multiclass classification training, then model accuracy is improved, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically performs clustering, identifies representative vectors, and selects the optimal subset for labeling. This automated self-service approach eliminates the need for manual intervention in the complex process of selecting which data points to label, making the overall process easier to operate while maintaining model accuracy.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational system that uses clustering algorithms and optimization techniques to identify representative vectors. This substitution of mechanical human effort with automated computational mechanisms significantly improves ease of operation while preserving the quality of labeled data needed for accurate models.
3Reliability
If more data is collected to address class imbalance in multiclass classification, then representation of all classes is improved, but the volume of data requiring labeling increases
Solution Approach 1:
The patent changes the parameter of data selection from random or uniform sampling to cluster-based representative sampling. By transforming the selection criterion to focus on cluster centers and representative vectors, the system can achieve balanced class representation with fewer data points. This parameter change in the selection strategy allows the model to handle class imbalance effectively without proportionally increasing the total data volume requiring labeling.
Data Source
AI summary
Systems and methods are described for training a machine learning model using intelligently selected multiclass vectors. According to an embodiment, a set of un-labeled feature vectors are received. The set of feature vectors are grouped into clusters within a vector space having fewer dimensions than the first set of feature vectors by applying a homomorphic dimensionality reduction algorithm to the set of feature vectors and performing centroid-based clustering. An optimal set of clusters among the clusters is identified by performing a convex optimization process on the clusters. Vector labeling is minimized by selecting ground truth representative vectors including a representative vector from each cluster of the optimal set of clusters. A set of labeled feature vectors is created based on labels received from an oracle for each of the representative vectors. A machine-learning model is trained for multiclass classification based on the set of labeled feature vectors.