Latent-Space Partitioning for Interpretable Measurement Data
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Solution Overview
Problem
Dimensionality reduction techniques in data sets result in latent spaces with properties that are difficult to interpret, hindering effective data analysis.
Innovation Solution
A computer-implemented method that partitions the latent space into subspaces, determines representative parameters for each subspace, and generates joint visualization data to facilitate interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If dimensionality reduction techniques are applied to reduce the number of dimensions in a data set, then the data set size is reduced and processing is facilitated, but the latent coordinates become fundamentally different from the original properties making interpretation difficult
Solution Approach 1:
The latent space is partitioned into multiple latent subspaces, each representing a specific region or cluster of data points. This segmentation allows operators to interpret data locally within each subspace rather than attempting to understand the entire latent space globally, thereby improving interpretability while maintaining dimensionality reduction benefits.
Solution Approach 2:
Representative parameters are introduced as intermediary elements that bridge the gap between latent coordinates and original data properties. These parameters serve as mediators that translate abstract latent space characteristics into meaningful interpretations related to the original data, making the reduced dimensionality data more understandable.
2Ease of operation
If latent space is partitioned into multiple subspaces and representative parameters are determined, then additional information is provided for better interpretation, but the processing complexity increases
Solution Approach 1:
By dividing the latent space into manageable subspaces, the system processes and interprets data in smaller, more tractable units. This segmentation reduces the cognitive load and computational complexity compared to analyzing the entire latent space as a single entity, while still providing comprehensive coverage through the collection of subspace analyses.
Solution Approach 2:
The system determines representative parameters for each subspace rather than attempting to compute all possible properties of all data points. This partial action approach provides sufficient information for interpretation without the excessive computational burden of complete analysis, achieving a practical balance between interpretability and complexity.
Data Source
AI summary
A computer-implemented data processing method includes the steps of: capturing and/or receiving a data set, wherein the data set includes a plurality of data points; reducing a dimensionality of the data set, thereby obtaining an adapted data set having a reduced dimensionality, wherein the adapted data set includes adapted data points, wherein the adapted data points have a predetermined number of latent coordinates, respectively, and wherein the latent coordinates are associated with a latent space; partitioning the latent space into a plurality of latent subspaces; determining at least one representative parameter for the plurality of latent subspaces, respectively, wherein the at least one representative parameter includes additional information on the adapted data points located in the respective latent subspace; and generating joint visualization data based on the adapted data set and based on the representative parameters determined. Further, a measurement system is described.