Repository holding various implementation of specific NMF methods for speaker diarization
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Updated
Feb 2, 2018 - Python
Repository holding various implementation of specific NMF methods for speaker diarization
Non-Negative Matrix Factorization for Gene Expression Clustering
Texture synthesis based on sparse representation.
Project is to analyze people’s sentiment and topics about the new administration. Used Twitter API to collect tweets about President Trump. Conduct sentiment analysis to measure how positive or negative the collected tweets are, which can be an indirect measure of President Trump’s approval. Find what kinds of topics are discussed related to the…
Nonnegative matrix factorization and matrix allocation algorithm implementations in C++
MATLAB code for stochastic variance reduced multiplicative updates (SVRMU) for NMF 1.0.0
X. Wang, Y. Zhong, L. Zhang, and Y. Xu, “Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp.6287-6304, 2017.
NMF/NTF with Pytorch
A python program that applies a choice of nonnegative matrix factorization (NMF) algorithms to a dataset for clustering.
Keras Non-Negative Matrix Factorization
Implementation of Bayesian Inference for Nonnegative Matrix Factorisation Models.Computational Intelligence and Neuroscience, 2009
Selected machine learning projects from courses I have taken during my PhD
Convolutive Matrix Factorization in Python
Sentimentally analyze product reviews to predict opinion honesty.
A gentle introduction to nonnegative matrix factorization (NMF), with an application to image compression 🖼️ 🎭 🎨
These codes are written as a part of ECE219 Large Scale Data Mining course at UCLA.
source code of my paper "Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization"
This project shows that companies often give the wrong name to different IT jobs. As a consequence, companies may fail to attract good candidates because an applicant has a significant probability to apply for the wrong job.
Theme Supervised Nonnegative Matrix Factorization
This is an implementation of the following paper (Algorithm1) in Python for image reconstruction. "N. Gillis and S.A. Vavasis, Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization"
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