Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
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Updated
Jun 21, 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
🪥 Unofficial re-implementation of Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
A library for machine learning and quantum programming based on pyRiemann and Qiskit projects
[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)
COVMOS is an open-source Python library designed for rapidly simulating catalogues of cosmic objects in both real and redshift space.
Evaluating machine learning methods for detecting sleep arousal, bachelor thesis by Jacob Stachowicz and Anton Ivarsson (2019)
Effect of hidden nodes on the reconstruction of bidirectional networks
A PyCUDA covariance matrix parallel implementation
SCFGP: Sparsely Correlated Fourier Features Based Gaussian Process
Given are two csv files, pc1.csv and pc2.csv, which contain noisy LIDAR point cloud data in the form of (x, y, z) coordinates of the ground plane. Find best surface fit
A Python front-end for the large-scale graphical LASSO optimizer BigQUIC (written in R).
Construction of PCA class from scratch and 3 implementations of PCA.
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.
Python implementation of Quantum Estimation Theory for a specific optomechanical system. Paper: https://arxiv.org/abs/2012.08876
Finding Covariance Matrix, Correlation Coefficient, Euclidean and Mahalanobis Distance
Implimentation of kalman filter for a vehicle with unknown location, noisy measurements using python
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.
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