TriMap: Large-scale Dimensionality Reduction Using Triplets. We introduce “TriMap”; a dimensionality reduction technique based on triplet constraints that preserves the global accuracy of the data better than the other commonly used methods such as t-SNE, LargeVis, and UMAP. To quantify the global accuracy, we introduce a score which roughly reflects the relative placement of the clusters rather than the individual points. We empirically show the excellent performance of TriMap on a large variety of datasets in terms of the quality of the embedding as well as the runtime. On our performance benchmarks, TriMap easily scales to millions of points without depleting the memory and clearly outperforms t-SNE, LargeVis, and UMAP in terms of runtime.
References in zbMATH (referenced in 2 articles )
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- Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
- Sainburg, Tim; Mcinnes, Leland; Gentner, Timothy Q.: Parametric UMAP embeddings for representation and semisupervised learning (2021)