Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
-
Updated
Jul 3, 2024 - Python
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
Scikit-learn compatible estimation of general graphical models
Fast, linear version of CorEx for covariance estimation, dimensionality reduction, and subspace clustering with very under-sampled, high-dimensional data
Ledoit-Wolf covariance matrix estimator of stock returns
A library for machine learning and quantum programming based on pyRiemann and Qiskit projects
🪥 Unofficial re-implementation of Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
A Python front-end for the large-scale graphical LASSO optimizer BigQUIC (written in R).
[CVPR2023] The official repository for paper "Learning Partial Correlation based Deep Visual Representation for Image Classification" To appear in 2023 The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)
Finding Covariance Matrix, Correlation Coefficient, Euclidean and Mahalanobis Distance
Construction of PCA class from scratch and 3 implementations of PCA.
Effect of hidden nodes on the reconstruction of bidirectional networks
COVMOS is an open-source Python library designed for rapidly simulating catalogues of cosmic objects in both real and redshift space.
A PyCUDA covariance matrix parallel implementation
SCFGP: Sparsely Correlated Fourier Features Based Gaussian Process
This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.
Implementation of STVNN from the paper "Spatiotemporal Covariance Neural Networks", ECML PKDD 2024
Evaluating machine learning methods for detecting sleep arousal, bachelor thesis by Jacob Stachowicz and Anton Ivarsson (2019)
Code for the paper E. Raninen and E. Ollila, “Coupled regularized sample covariance matrix estimator for multiple classes,” in IEEE Transactions on Signal Processing, vol. 69, pp. 5681–5692, 2021, doi: 10.1109/TSP.2021.3118546.
Implimentation of kalman filter for a vehicle with unknown location, noisy measurements using python
Python implementation of Quantum Estimation Theory for a specific optomechanical system. Paper: https://arxiv.org/abs/2012.08876
Add a description, image, and links to the covariance-matrix topic page so that developers can more easily learn about it.
To associate your repository with the covariance-matrix topic, visit your repo's landing page and select "manage topics."