Gaussian Mixture Model with low rank approximation
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Updated
Jun 2, 2020 - Python
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
Coursework containing but not limited to the course Intro to Data Science
Small project on numerical linear algebra
Alternating projections for constrained low-rank approximation of matrices and tensors.
A Fortran library for working with low-rank matrices and tensors.
Assignments of Data Science Class
Low Rank Approximation (Adaptation) Methods in Neural Networks
IE 531 - Algorithms for Data Analytics. A detailed description of each assignment is provided.
The repository contains implementation of some data science algorithms
Codes for the paper: Theoretical bounds on the network community profile from low-rank semi-definite programming
Projet for a course on Low Rank Approximation Techniques
Repository for implementation details for Data-Science
Nystrom Low Rank Gram Matrix Approximation in KELP
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.
A smoothing proximal gradient algorithm for matrix rank minimization problem
Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics
Cartoon-texture image decomposition using blockwise low-rank texture characterization
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