Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
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Updated
Apr 18, 2023 - R
Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
🍱 R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow)
R Package: Regularized Principal Component Analysis for Spatial Data
Computational Methods for Numerical Analysis
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation in R and Python
Factoried Personalized Markov Chains for Next Basket Recommendation in R and Python
mfair: Matrix Factorization with Auxiliary Information in R
Matrix factorization-based biological discovery from large-scale transcriptome data using easyMF
R interface to the fastFM library
Probabilistic Matrix Factorization with JAGS in R
R package implementing Bayesian NMF using various models and prior structures.
Using Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。
This contains implementation of eigenvalue calculation algorithms from scratch.
Low Rank Matrix Factorization S3 Objects
Interpretive Structural Modelling (ISM). Returns a minimum-edge hierarchical digraph following J.N. Warfield's graph partitioning algorithm.
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 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 Package: Regularized Principal Component Analysis for Spatial Data
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。
Product Recommender Engine - Use Case: 'The MovieLens 10M dataset'
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