Dimensionality Reduction via Regression-Updated Distance Functions

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Solution Overview

Problem

Existing data dimensionality reduction methods require user interaction to adjust data points and input distance information, leading to increased computing load and processing time.

Innovation Solution

A method that dimensionally reduces high-dimensional data using a distance function with adjustable parameters, performing regression analysis in subspaces to update these parameters automatically, reducing the need for user input and minimizing processing time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If interactive adjustment of data points and distance information is performed to obtain appropriate dimension reduction results, then the quality of dimension reduction results is improved, but the computing load and processing time increase

Engineering Contradiction:

Improvequality of dimension reduction resultsVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-defining multiple candidate distance functions with different parameter combinations before the dimension reduction process. This allows the system to automatically evaluate and select the most appropriate distance function without requiring interactive user adjustment during the process, thereby reducing processing time while maintaining result quality

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements self-service through an automated evaluation mechanism that assesses the performance of different distance functions using evaluation indices (such as silhouette coefficient or Davies-Bouldin index). The system automatically selects the optimal distance function based on these indices without requiring user intervention, thus reducing both computing load and processing time while maintaining high quality results

Inventive Principle:

Principle #25Self-service

2Measurement precision

Engineering Contradiction:

Improvequality of dimension reduction resultsVSAvoidcomputing load

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent reduces computing load by pre-defining candidate distance functions with different parameter combinations before the dimension reduction process. This preliminary preparation allows the system to efficiently evaluate and select the optimal function without requiring extensive interactive adjustments during execution, thereby reducing overall computing resource consumption

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements self-service through an automated evaluation and selection mechanism that independently assesses candidate distance functions using evaluation indices. This automation eliminates the need for iterative user interaction and manual adjustment, significantly reducing the computing load while maintaining or improving the quality of dimension reduction results

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12253993B2Data dimensionality reduction method, computer program, and data dimensionality reduction device

Publication Date: 2025.03.18 UACJ CORP

AI summary

A data dimensionality reduction method includes: a step of dimensionally reducing a group of data from a high-dimensional space to a low-dimensional space using a distance function that defines a distance between any two vectors in the high-dimensional space; a step of dividing the dimensionally-reduced low-dimensional space into multiple subspaces; an analysis step of performing a regression analysis using a regression model based on at least one belonging data for each divided subspace; and a step of updating p first parameters included in the distance function based on results of the regression analysis in the multiple subspaces.