Unsupervised Training Set Generation via Document Clustering
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Solution Overview
Problem
Supervised machine learning requires large volumes of labeled training data, which is time-consuming and expensive to generate, and existing sampling methods may produce unsatisfactory results due to lack of semantic diversity and the need for initial labeled examples.
Innovation Solution
The method generates semantically representative samples by processing documents to produce vector representations, clustering them, and selecting documents from clusters to create a diverse training set without requiring manual labeling or initial labels, using techniques like latent Dirichlet allocation and k-means clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to generate training data, then the quality and accuracy of labeled training data is improved, but the time consumption and cost increase significantly
Solution Approach 1:
The system performs self-service by automatically generating labels through unsupervised learning algorithms (clustering, topic modeling) without requiring manual human intervention. The model autonomously processes unlabeled data, creates cluster assignments as pseudo-labels, and iteratively improves, thereby eliminating the time-consuming and expensive manual labeling process while maintaining labeling quality through algorithmic consistency.
Solution Approach 2:
The patent replaces the mechanical human labeling process with automated computational algorithms. Instead of relying on human experts to manually assign labels (a mechanical human-centric process), the system uses unsupervised learning algorithms like k-means clustering, hierarchical clustering, and topic modeling to automatically generate labels, substituting human mechanical effort with automated computational mechanisms.
2Productivity
If random sampling is used to create training sets, then the process is simple and fast, but the semantic diversity and representativeness of the training data deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing the unlabeled data through clustering and topic modeling before final training set selection. This preliminary organization of data into semantically meaningful groups (clusters and topics) ensures that subsequent sampling maintains semantic diversity and representativeness, rather than sampling directly from raw unlabeled data which would lack structure.
Solution Approach 2:
The patent applies local quality by ensuring that different regions of the data space (different clusters and topics) are appropriately represented in the training set. Instead of uniform random sampling, the system strategically selects samples from different semantic regions (clusters/topics) to ensure each local region contributes proportionally to the training set, thereby maintaining overall semantic diversity and representativeness.
3Ease of manufacture
If existing sampling methods are used, then the process requires fewer resources, but the quality of training data deteriorates due to lack of semantic understanding
Solution Approach 1:
The patent replaces simple mechanical sampling methods with intelligent algorithms that incorporate semantic understanding through unsupervised learning. Instead of using basic random or stratified sampling mechanisms, the system employs clustering algorithms (k-means, hierarchical), topic modeling (LDA), and embedding-based methods that automatically discover and utilize semantic structures in the data, thereby improving data quality without requiring manual intervention.
Solution Approach 2:
The system applies parameter changes by transforming the sampling approach from fixed predetermined criteria to dynamic parameters derived from data analysis. The sampling process adapts to the actual semantic structure of the data by using cluster assignments, topic distributions, and embedding similarities as dynamic parameters, allowing the sampling strategy to automatically adjust to the data's inherent semantic organization rather than relying on static predetermined parameters.
Data Source
AI summary
A computer system trains a machine learning model. A vector representation is generated for each document in a collection of documents. The documents are clustered based on the vector representations of the documents to produce a plurality of clusters. A training set is produced by selecting one or more documents from each cluster, wherein the selected documents represent a sample of the collection of documents to train the machine learning model. The machine learning model is trained by applying the training set to the machine learning model. Embodiments of the present invention further include a method and program product for training a machine learning model in substantially the same manner described above.