A JAX implementation of TriMap: examples using the current implementation will be added soon.

TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet constraints are of the form "point i is closer to point j than point k". The triplets are sampled from the high-dimensional representation of the points and a weighting scheme is used to reflect the importance of each triplet.

TriMap provides a significantly better global view of the data than the other dimensionality reduction methods such t-SNE, LargeVis, and UMAP. The global structure includes relative distances of the clusters, multiple scales in the data, and the existence of possible outliers.

Example usage:

import jax.random as random
from sklearn.datasets import load_digits
import trimap

digits = load_digits()
key = random.PRNGKey(42)

embedding = trimap.transform(key, digits.data, distance='euclidean')

Source:

Paper: https://arxiv.org/pdf/1910.00204.pdf
Github: https://github.com/eamid/trimap

Reference:

@article{2019TRIMAP, author = {{Amid}, Ehsan and {Warmuth}, Manfred K.}, title = "{TriMap: Large-scale Dimensionality Reduction Using Triplets}", journal = {arXiv preprint arXiv:1910.00204}, archivePrefix = "arXiv", eprint = {1910.00204}, year = 2019, }