paper-with-me

Papers

DADApy: Distance-based Analysis of DAta-manifolds in Python

2022-05-04 · Aldo Glielmo, Iuri Macocco, Diego Doimo, Matteo Carli, Claudio Zeni, Romina Wild, Maria d'Errico, Alex Rodriguez, Alessandro Laio

DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in toy cases and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.

📄 PDF Abstract BibTeX arXiv:2205.03373

Code (1)

sissa-data-science/dadapy 공식 구현 jax

Tasks

Clustering

Similar Papers 제목 키워드 기반

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance

2024-10-30 · Romina Wild, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng 외

Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essen…

feature selection

geomstats: a Python Package for Riemannian Geometry in Machine Learning

2018-05-21 · ICLR 2019 5 · Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda 외

We introduce geomstats, a python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups of transformations. We provide effic…

BIG-bench Machine LearningGPURiemannian optimization

Riemannian Principal Component Analysis

2025-05-30 · Oldemar Rodríguez

This paper proposes an innovative extension of Principal Component Analysis (PCA) that transcends the traditional assumption of data lying in Euclidean space, enabling its application to data on Riemannian manifolds. The…

Dimensionality Reduction

Metric Learning on Manifolds

2019-02-05 · Max Aalto, Nakul Verma

Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are genera…

ClusteringMetric Learning

Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds

2023-10-09 · Daniel Kelshaw, Luca Magri

Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and provide a notion of distance, which are ke…