Semi-supervised User Classification via Embedding Space Segmentation
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Solution Overview
Problem
Existing machine learning techniques struggle with effectively classifying data when faced with a large amount of unlabeled data and a smaller amount of labeled data, as they either rely solely on labeled data, which can be costly or time-consuming to collect, or use unsupervised methods that may not accurately separate data points as intended.
Innovation Solution
The implementation of semi-supervised machine learning methods that utilize both labeled and unlabeled data by training neural networks to map user information into an embedding space and then applying low-density separation algorithms to predict category membership propensities, iteratively refining decision boundaries to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used with labeled training data, then classification accuracy can be achieved, but the process is costly and time-consuming due to the need to collect and label large amounts of data
Solution Approach 1:
The patent combines supervised and unsupervised learning approaches into a hybrid semi-supervised framework. The system integrates labeled training data with unlabeled data by using autoencoders to learn representations from unlabeled data and then combines these with supervised learning from labeled data, allowing the model to leverage both sources effectively and reducing the dependency on large labeled datasets
Solution Approach 2:
The patent uses autoencoders to create compressed representations (embeddings) of data from unlabeled examples. These representations serve as pseudo-labels or preliminary classifications that can be used to train or fine-tune the main classification model, effectively copying the structure and patterns from unlabeled data to supplement the limited labeled data
2Loss of time
If unsupervised learning is used to process unlabeled data, then the process is more efficient and less costly, but the method may not accurately separate data points as intended
Solution Approach 1:
The patent implements an iterative feedback mechanism where the autoencoder generates initial representations from unlabeled data, these are used to train a classification model, the model then processes the same unlabeled data to generate refined predictions, and this cycle repeats. The feedback loop continuously improves the accuracy of data separation by comparing and refining the autoencoder's initial unsupervised representations with the supervised classification results
Solution Approach 2:
The patent uses autoencoders to perform preliminary processing of unlabeled data by learning compressed representations before the main classification task. This preliminary action creates a foundation that guides subsequent supervised learning, allowing the system to prepare and pre-organize unlabeled data in a way that improves final classification accuracy without requiring full supervised training from scratch
3Measurement precision
If semi-supervised learning is implemented to utilize both labeled and unlabeled data, then classification accuracy improves and reliance on labeled data reduces, but the system complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct functional components: an autoencoder module for unsupervised representation learning from unlabeled data, a classification module for supervised learning from labeled data, and a integration module that combines both approaches. This segmentation allows each component to specialize in specific tasks, making the overall complex semi-supervised system more manageable and maintainable by dividing it into smaller, well-defined subsystems
Data Source
AI summary
Systems and methods are described for user classification with semi-supervised machine learning. The systems and methods may include receiving user information for a first set of users, receiving survey data for a second set of users wherein the second set of users is a proper subset of the first set of users, training a first neural network and a second neural network based on the second set of users, mapping the user information for the first set of users to the embedding space using the first neural network, predicting category membership propensities for the first set of users using a low-density separation algorithm on the user information for the first set of users mapped to the embedding space, updating the first neural network and the second neural network based on the prediction, and reclassifying the first set of users based on the updated first neural network and the updated second neural network.