DimenFix: A novel meta-dimensionality reduction method for feature preservation

TL;DR

DimenFix is a meta-dimensionality reduction method that enhances existing techniques by incorporating feature importance, enabling better dataset visualization without additional computational cost.

Contribution

It introduces a flexible meta-method that allows feature importance to be integrated into any gradient-based dimensionality reduction technique.

Findings

Preserves data relationships effectively.

Allows user-defined feature importance.

Maintains computational efficiency.

Abstract

Dimensionality reduction has become an important research topic as demand for interpreting high-dimensional datasets has been increasing rapidly in recent years. There have been many dimensionality reduction methods with good performance in preserving the overall relationship among data points when mapping them to a lower-dimensional space. However, these existing methods fail to incorporate the difference in importance among features. To address this problem, we propose a novel meta-method, DimenFix, which can be operated upon any base dimensionality reduction method that involves a gradient-descent-like process. By allowing users to define the importance of different features, which is considered in dimensionality reduction, DimenFix creates new possibilities to visualize and understand a given dataset. Meanwhile, DimenFix does not increase the time cost or reduce the quality of dimensionality reduction with respect to the base dimensionality reduction used.