Latent Space Segmentation for Multi-Dimensional Risk Prediction
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Solution Overview
Problem
Decision-making under uncertainty in real-world scenarios is challenging due to the complexity of assessing and predicting risk with multiple variables affecting outcomes, particularly in high-stakes situations where incomplete knowledge is prevalent.
Innovation Solution
A computer-implemented method and system using a machine learning model to perform operations on multi-dimensional functions by mapping input space to latent vectors, splitting them into lower-dimensional groupings, performing operations in latent space, and combining results for prediction, employing techniques like Gaussian processes and Bayesian deep neural networks to quantify risk and inform decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to assess and predict risk with multiple variables, then decision-making can proceed, but the accuracy and reliability of risk assessment deteriorates due to the complexity of handling multiple variables
Solution Approach 1:
The patent applies segmentation by decomposing the high-dimensional input space into multiple lower-dimensional latent spaces through a hierarchical projection architecture. Each layer of the hierarchy processes a subset of variables independently, breaking down the complex multi-variable risk assessment into manageable segments that can be evaluated separately and then combined.
Solution Approach 2:
The patent transforms the problem from high-dimensional input space to lower-dimensional latent spaces through learned projections. This dimensionality reduction maintains the essential relationships among variables while simplifying the computational complexity, enabling accurate risk assessment without being overwhelmed by the number of variables.
2Loss of information
If high-dimensional data is processed directly, then complete information is available, but computational efficiency and scalability deteriorates
Solution Approach 1:
The hierarchical projection architecture segments the high-dimensional data processing task into multiple layers, where each layer handles a portion of the dimensionality reduction. This segmentation enables parallel processing of different variable subsets, improving computational efficiency while preserving information through the hierarchical structure.
Solution Approach 2:
The patent performs preliminary dimensionality reduction through learned projections before conducting the main risk assessment operations. This preliminary action transforms the data into a more manageable form that retains essential information, making subsequent computations more efficient without losing critical risk-related information.
3Measurement precision
If complex multi-variable models are used to capture all risk factors, then prediction accuracy improves, but model interpretability and ease of operation deteriorates
Solution Approach 1:
The hierarchical projection architecture segments the complex model into interpretable layers, where each layer captures specific relationships among subsets of variables. This segmentation maintains prediction accuracy by preserving variable interactions while enabling interpretability through the structured, modular architecture that can be analyzed layer by layer.
Solution Approach 2:
The patent projects high-dimensional variable relationships into lower-dimensional latent spaces where the essential patterns remain visible and interpretable. This dimensionality change simplifies the model structure while maintaining predictive power, making it easier to understand and operate without sacrificing accuracy.
Data Source
AI summary
A system and method for performing operations on multi-dimensional functions using a machine learning model, the method including: receiving a problem formulation in input space; mapping the problem formulation from input space to one or more latent vectors or a set in latent feature space using a projection learned using the machine learning model; splitting the one or more latent vectors or set in latent space into a plurality of lower-dimensional groupings of latent features; performing one or more operations in latent space on each lower-dimensional groupings of latent features; combining each of the low-dimensional groupings; and outputting the combination for generating the prediction.