A recommender system using low-rank approximation and stock market prediction using Mote Carlo simulation
-
Updated
Mar 15, 2017 - TeX
A recommender system using low-rank approximation and stock market prediction using Mote Carlo simulation
My experiment of multilayer NMF, a deep neural network in which the first several layers take Semi-NMF as its pseudo-activation-function that finds the latent sturcture embedding in the original data unsupervisely.
Cartoon-texture image decomposition using blockwise low-rank texture characterization
Coursework containing but not limited to the course Intro to Data Science
Approximate Ridge Linear Mixed Models (arLMM)
Linear Algebra project `Decomposition into Low-Rank and Sparse Matrices in Computer Vision` | Applied Sciences Faculty, UCU (2019)
Implementation of Collective Matrix Completion by Mokhtar Z. Alaya and Olga Klopp https://arxiv.org/abs/1807.09010
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising, ICCV 2017.
Nystrom Low Rank Gram Matrix Approximation in KELP
Caffe for Sparse and Low-rank Deep Neural Networks
Gaussian Mixture Model with low rank approximation
IE 531 - Algorithms for Data Analytics. A detailed description of each assignment is provided.
Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition
MUSCO: Multi-Stage COmpression of neural networks
Tutorial reimplementation of Monteiro et al. (2020) on a toy problem.
Convolutive Matrix Factorization in Julia
Multi-slice MR Reconstruction with Low-Rank Tensor Completion
Assignments of Data Science Class
Repository for implementation details for Data-Science
Add a description, image, and links to the low-rank-approximation topic page so that developers can more easily learn about it.
To associate your repository with the low-rank-approximation topic, visit your repo's landing page and select "manage topics."