Autoencoder Invariant Data Representation for Diverse Sources
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Solution Overview
Problem
Existing methods struggle to effectively harness and standardize data from diverse Internet sources for predicting conditions, such as health-related states, due to varying usage patterns and changing data formats across different platforms and services.
Innovation Solution
A system utilizing neural networks, including autoencoder and adversarial neural networks, generates invariant representations of content data elements from multiple Internet-based systems, allowing for unsupervised training and prediction of conditions independent of data sources, using a latent space that is invariant to the origin of the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple Internet-based systems is aggregated for prediction, then the quantity and diversity of data increases, but the complexity of handling different data sources and formats increases
Solution Approach 1:
The system segments the complex task of data integration by creating separate autoencoder neural networks for each data source type. Each autoencoder learns the specific characteristics and transformations needed for its source, breaking down the overall complexity into manageable modular components that can be independently trained and combined.
Solution Approach 2:
The patent introduces an intermediary latent space that serves as a common representation layer between diverse data sources and the prediction model. This latent space acts as a mediator that standardizes different data formats and structures into a unified representation, enabling the prediction model to process heterogeneous data without directly handling source-specific complexities.
2Adaptability or versatility
If standardized datasets are extracted from diverse sources, then data compatibility improves, but information loss may occur during standardization
Solution Approach 1:
The autoencoder neural networks transform data by changing parameters and representations rather than simple standardization. The encoders learn optimal parameter transformations for each data source that preserve essential information while adapting to a common format. The latent space representation captures meaningful variations without losing critical predictive information.
3Loss of time
If unsupervised training is used, then training time and computational resources are reduced, but the precision of invariant representation may be compromised
Solution Approach 1:
The adversarial training mechanism introduces feedback loops where the discriminator provides gradient feedback to the autoencoders to improve their invariant representation. This feedback-driven approach allows the system to iteratively refine the latent space representation, achieving higher precision in capturing source-invariant features while maintaining the efficiency of unsupervised training frameworks.
Data Source
AI summary
A system and method for predicting a condition of a subject may include one or more autoencoder modules, trained to: receive at least one content data element pertaining to the subject from one or more data sources of a plurality of data sources; and generate a source-invariant representation of the at least one content data element in a latent space of the one or more autoencoders. One or more machine-learning (ML) based classification models may receive the source-invariant representation of the at least one content data element, and produce therefrom a prediction data element, which may represent a predicted condition of the subject.