R package implementing Bayesian NMF using various models and prior structures.
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Updated
Jun 12, 2024 - R
R package implementing Bayesian NMF using various models and prior structures.
R Package: Regularized Principal Component Analysis for Spatial Data
R Package: Regularized Principal Component Analysis for Spatial Data
mfair: Matrix Factorization with Auxiliary Information in R
Computational Methods for Numerical Analysis
Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
This contains implementation of eigenvalue calculation algorithms from scratch.
Matrix factorization-based biological discovery from large-scale transcriptome data using easyMF
Code to reproduce Adaptive elastic-net sparse PCA for robust cross-species, cross-platform analysis of complex gene expression data in Alzheimer’s disease (Hin et al.)
The purpose of the present project is to create a recommendation system for predicting the rating of movies.
Low Rank Matrix Factorization S3 Objects
Interpretive Structural Modelling (ISM). Returns a minimum-edge hierarchical digraph following J.N. Warfield's graph partitioning algorithm.
Product Recommender Engine - Use Case: 'The MovieLens 10M dataset'
Using Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。
Probabilistic Matrix Factorization for Recommendation by R。使用R语言实现了矩阵分解、概率矩阵分解算法。
The main task of a recommender system is to predict the users responce to different options. This is my solution for the first capstone project in the course 'Professional Certificate in Data Science' provided by Harvard University (HarvardX) on EDX.
🍱 R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow)
Factoried Personalized Markov Chains for Next Basket Recommendation in R and Python
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation in R and Python
Probabilistic Matrix Factorization with JAGS in R
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