From 0d888c54c17656a4678cb36f459c7dce5efcb71c Mon Sep 17 00:00:00 2001 From: Caleb Geniesse Date: Tue, 16 Aug 2022 16:56:41 -0700 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 96dbb93..6e88f16 100644 --- a/README.md +++ b/README.md @@ -19,9 +19,9 @@ For more details about NeuMapper see "[NeuMapper: A Scalable Computational Frame ## **Related Projects** -- [Reciprocal Isomap](https://calebgeniesse.github.io/reciprocal_isomap) is a Python version of the reciprocal variant of Isomap for robust non-linear dimensionality reduction. `ReciprocalIsomap` was inspired by scikit-learn's implementation of Isomap, but the reciprocal variant enforces shared connectivity in the underlying *k*-nearest neighbors graph (i.e., two points are only considered neighbors if each is a neighbor of the other). +- [Reciprocal Isomap](https://calebgeniesse.github.io/reciprocal_isomap) is a reciprocal variant of Isomap for robust non-linear dimensionality reduction in Python. The `ReciprocalIsomap` transformer was inspired by scikit-learn's implementation of Isomap, but the reciprocal variant enforces shared connectivity in the underlying *k*-nearest neighbors graph (i.e., two points are only considered neighbors if each is a neighbor of the other). -- [Landmark Cover](https://calebgeniesse.github.io/landmark_cover) is a Python version of the landmark-based cover for Mapper. `LandmarkCover` was designed for use with [KeplerMapper](https://kepler-mapper.scikit-tda.org/en/latest/), but rather than dividing an *extrinsic* space (e.g., low-dimensional projection) into overlapping hypercubes, the landmark-based approach directly partitions data points into overlapping subsets based on their *intrinsic* distances from pre-selected landmark points. +- [Landmark Cover](https://calebgeniesse.github.io/landmark_cover) is a Python implementation of NeuMapper's landmark-based cover. The `LandmarkCover` transformer was designed for use with [KeplerMapper](https://kepler-mapper.scikit-tda.org/en/latest/), but rather than dividing an *extrinsic* space (e.g., low-dimensional projection) into overlapping hypercubes, the landmark-based approach directly partitions data points into overlapping subsets based on their *intrinsic* distances from pre-selected landmark points. - [DyNeuSR](https://braindynamicslab.github.io/dyneusr/) is a Python library for visualizing topological representations of neuroimaging data. The package combines visual web components with a high-level Python interface for interacting with, manipulating, and visualizing topological graph representations of functional brain activity.