TriMap: Large-scale Dimensionality Reduction Using Triplets
This provides a more efficient and scalable solution for data visualization and analysis in machine learning, though it is incremental as it builds on existing triplet-based approaches.
The paper tackles the problem of preserving global structure in large-scale dimensionality reduction by introducing TriMap, a technique based on triplet constraints, which outperforms methods like t-SNE, LargeVis, and UMAP in runtime and scales to millions of points without memory depletion.
We introduce "TriMap"; a dimensionality reduction technique based on triplet constraints, which preserves the global structure of the data better than the other commonly used methods such as t-SNE, LargeVis, and UMAP. To quantify the global accuracy of the embedding, we introduce a score that 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.