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Found 3 papers, 1 papers with code
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction
In this paper, we introduce two novel quality measures -- Label-Trustworthiness and Label-Continuity (Label-T&C) -- advancing the process of DR evaluation based on class labels.
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns
Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data.
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data
In this paper, we present a methodology that utilizes multiple machine learning methods to uniformly explore these aspects.
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