Uniform Manifold Approximation with Two-phase Optimization

Abstract

We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately. In UMATO, optimization is divided into two phases so that the resulting embeddings can depict the global structure reliably while preserving the local structure with sufficient accuracy. In the first phase, hub points are identified and projected to construct a skeletal layout for the global structure. In the second phase, the remaining points are added to the embedding preserving the regional characteristics of local areas. Through quantitative experiments, we found that UMATO (1) outperformed widely used DR techniques in preserving the global structure while (2) producing competitive accuracy in representing the local structure. We also verified that UMATO is preferable in terms of robustness over diverse initialization methods, number of epochs, and subsampling techniques.

arXiv commentIEEE VIS 2022. Hyeon Jeon and Hyung-Kwon Ko equally contributed to this work

References in corpus (11)

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• LINE: Large-scale Information Network Embedding
• Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
• Visualizing Large-scale and High-dimensional Data
• t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
• Robust Topological Inference: Distance To a Measure and Kernel Distance
• Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
• Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study
• Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections
• HUMAP: Hierarchical Uniform Manifold Approximation and Projection

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