Codes for the paper: Theoretical bounds on the network community profile from low-rank semi-definite programming
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Updated
Oct 19, 2023 - Julia
Codes for the paper: Theoretical bounds on the network community profile from low-rank semi-definite programming
Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics
Projet for a course on Low Rank Approximation Techniques
Gaussian Mixture Model with low rank approximation
A MATLAB implementation of "Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares".
A recommender system using low-rank approximation and stock market prediction using Mote Carlo simulation
Repository for implementation details for Data-Science
Implementation of Collective Matrix Completion by Mokhtar Z. Alaya and Olga Klopp https://arxiv.org/abs/1807.09010
Alternating projections for constrained low-rank approximation of matrices and tensors.
Nystrom Low Rank Gram Matrix Approximation in KELP
Calibrationless Multi-Slice Cartesian MRI via Orthogonally Alternating Phase Encoding Direction and Joint Low-Rank Tensor Completion
This repository contains MATLAB files for the implementation of work proposed in the paper Efficient Structure-preserving Support Tensor Train Machine.
A Fortran library for working with low-rank matrices and tensors.
Assignments of Data Science Class
Low Rank Approximation (Adaptation) Methods in Neural Networks
Methods for label-free mass spectrometry proteomics
Introducing traditional algorithms in Recommendation System.
The repository contains code to reproduce the experiments from our paper Error Feedback Can Accurately Compress Preconditioners available below:
Numerical experiments for Optima-TT method from teneva python package. This method finds items which relate to min and max elements of the tensor in the tensor train (TT) format.
Toolbox allows to test and compare methods for Image Completion and Data Completion problems in Matlab. Presented methods use various Nonnegative Matrix Factorization and Tensor decomposition algorithms. It was based on research performed during realization of PhD.
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