Tensor Amplification-Based Processing for Stable Medical Decomposition
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Solution Overview
Problem
Existing tensor-based methods for processing high-dimensional multi-modal data face challenges such as computational complexity, memory requirements, and non-unique canonical polyadic decompositions, making them unsuitable for sensitive applications like medical diagnostics.
Innovation Solution
Tensor amplification-based methods that amplify low rank structure through algebraic computations, providing numerically stable decompositions for efficient processing and de-noising of high-dimensional data, enabling effective medical condition assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tensor amplification-based decomposition is used to amplify low rank structure, then decomposition stability and uniqueness are improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary actions by performing tensor amplification transformations before decomposition to enhance the low-rank structure of the data. This preprocessing step amplifies the signal components and suppresses noise, making the subsequent decomposition more stable and unique without requiring excessive computational resources during the main analysis phase.
Solution Approach 2:
The patent changes parameters by introducing amplification factors and transformation parameters that modify the tensor structure to emphasize low-rank components. By adjusting these parameters, the decomposition becomes more stable and reliable while managing computational complexity through efficient algorithmic implementations.
2Loss of information
If multi-modal data structure is retained, then data information completeness is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the multi-modal data into separate tensor factors that represent different data sources or modalities. This factorization allows the system to process and analyze each component independently while preserving the overall data structure, thereby reducing processing time without losing information completeness.
Solution Approach 2:
The patent transforms the multi-modal data from a high-dimensional tensor into a lower-dimensional representation through factorization into multiple smaller tensors. This dimensionality reduction technique preserves the essential information while significantly reducing the computational burden of processing.
3Productivity
If feature dimensionality is reduced, then processing efficiency is improved, but classification accuracy may deteriorate
Solution Approach 1:
The patent extracts the most significant features from the high-dimensional data by performing tensor decomposition and selecting the dominant factors. This extraction process removes redundant and noisy components while retaining the essential information needed for accurate classification, thereby improving processing efficiency without sacrificing accuracy.
Solution Approach 2:
The patent optimizes the balance between dimensionality reduction and accuracy preservation by adjusting the number of retained factors and applying regularization parameters. These parameter changes allow the system to achieve efficient processing while maintaining or even improving classification accuracy through denoising and feature enhancement.
Data Source
AI summary
A method of generating an assessment of medical condition for a patient includes obtaining a patient data tensor indicative of a plurality of tests conducted on the patient, obtaining a set of tensor factors, each tensor factor of the set of tensor factors being indicative of a decomposition of training tensor data for the plurality of tests, the decomposition amplifying low rank structure of the training tensor data, determining a patient tensor factor for the patient based on the obtained patient data tensor and the obtained set of tensor factors, applying the determined patient tensor factor to a classifier such that the determined further tensor factor establishes a feature vector for the patient, the classifier being configured to process the feature vector to generate the assessment, and providing output data indicative of the assessment.