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matrix-and-tensor-completion-algorithm

matrix-completion

An ADMM + Compressed Sensing algorithm to estimate a low-rank sparse matrix

matrix-completion-1

matrix-completion-2

Some classical matrix completion algorithms.

matrix-completion-3

The codes with respect to matrix completion

matrix-completion-by-deep-matrix-factorization

The MATLAB code for the algorithm

matrixcompletion

Study and application of Matrix Completion approach on various Big Data sets

matrixcompletion-1

Implementing a simple algorithm for matrix completion and doing a performance comparison against the standard convex method

matrixcompletion-2

Contains scripts for performing and comparing matrix completion methods on Netflix Prize data

matrixcompletion-3

Can be used for movie preference prediction.

matrixirls

Matrix Iteratively Reweighted Least Squares for low-rank matrix completion and estimation

mckit

MATLAB library for Matrix Completion

mclpmda

MCLPMDA: A novel method for miRNA-disease association prediction based on Matrix Completion and Label Propagation

mcos

Robust Matrix Completion with outliers and sparse noise

mctc4bmi

Matrix and Tensor Completion for Background Model Initialization

mdaep-sr

Learning Multi-Denoising Autoencoding Priors for Image Super-Resolution

metric-matlab

Matlab Implementation of METRIC for Landsat 7 & 8 Remote Sensing Images

mfm-dev

MFM community development code

mgsee

In the hyperspectral unmixing literature, endmember extraction is addressed majorly using three methods i.e. Statistical, Sparse-regression and Geometrical. The majority of the endmember extraction algorithms are developed based on only one of the methods. Recently, GSEE (Geo-Stat Endmember Extraction) has been proposed that combines the geometrical and statistical features. In this paper, we propose a Modified GSEE (MGSEE) algorithm which considers the removal of noisy bands. In the proposed work, the Minimum Noise Fraction (MNF) is used to select high SNR bands. The strength of the MGSEE framework is scrutinized using a synthetic and real benchmark dataset. In this paper, we show that the proposed algorithm obtained from the GSEE by preceding the noise removal step greatly decreases Spectral Angle Error (SAE) and Spectral Information Divergence (SID) error thus indicating its importance to extract pure material in the unmixing problem.

mhf-net

Code of Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net

mircom

The source code of the paper “Pei, L.,Luo J.W. miRCom: Tensor completion integrating multi-view information to deduce the potential disease-related miRNA pairs”

mixed-noise-removal-in-hyperspectral-image-via-low-fibered-rank-regularization

code of Mixed Noise Removal in Hyperspectral Image via Low-Fibered-Rank Regularization

ml_in_medical_imaging_exercises

Machine Learning in Medical Imaging - Exercises.

mmle-gmm

Compressive sensing by leanring (low-rank) GMM from measurements

mmsr

The code for NeurIPS 2020 paper: Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion.

moamn

mr-amp

Multi-resolution approximate message passing algorithm for multi-resolution compressed sensing problem

mra-mgg-softmax

An adaptive variational model for multireference alignment with mixed noise

mrf-enermin

A MATLAB package for energy minimization in Markov random field using Graph Cuts.

mrkcs

Multi-resolution/Multi-scale Kronecker compressive sensing, IEEE Inter. Conf. Image Process. (ICIP) 2015