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Dimensionality reduction has become an important research topic as demand forinterpreting high-dimensional datasets has been increasing rapidly in recentyears. There have been many dimensionality reduction methods with goodperformance in preserving the overall relationship among data points whenmapping them to a lower-dimensional space. However, these existing methods failto incorporate the difference in importance among features.  To address this problem, we propose a novel meta-method, DimenFix, which canbe operated upon any base dimensionality reduction method that involves agradient-descent-like process. By allowing users to define the importance ofdifferent features, which is considered in dimensionality reduction, DimenFixcreates new possibilities to visualize and understand a given dataset.Meanwhile, DimenFix does not increase the time cost or reduce the quality ofdimensionality reduction with respect to the base dimensionality reductionused.

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