Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation
By John Aldo Lee, Emilie Renard, Guillaume Bernard, P. Dupont, Michel Verleysen. 2013
102 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Leland McInnes
- John Aldo Lee
- John Healy
- Michel Verleysen
- Cyril de Bodt
- Dounia Mulders
- Maarten Grootendorst
- D. Angelov
In Computer Vision and Pattern Recognition
- Leland McInnes
- John Aldo Lee
- Michel Verleysen
- Cyril de Bodt
- Dounia Mulders
- John Healy
- Mohammad Rostami
- D. Angelov
In Representation & Self-Supervised Learning
- Leland McInnes
- John Healy
- Michel Verleysen
- John Aldo Lee
- Cyril de Bodt
- Dounia Mulders
- Mohammad Rostami
- Maarten Grootendorst
Citations in the corpus
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018, methodology): “Algorithms such as PCA [8], MDS [9], and Sammon mapping [20] fall into the former category while t-SNE [15] [26], Isomap [24], LargeVis [22], Laplacian eigenmaps [1] [2], diffusion maps [4], NeRV [28], and JSE [12] all fall into the latter category.”
- Perplexity-free t-SNE and twice Student tt-SNE (2018, background): “Variants with alternative divergences as cost functions [7, 8, 9] and lower-complexity tree-based approximations [10, 11] have been developed.”