R Package: Regularized Principal Component Analysis for Spatial Data
-
Updated
May 25, 2024 - R
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'
The purpose of the present project is to create a recommendation system for predicting the rating of movies.
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.
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.
R package implementing Bayesian NMF using various models and prior structures.
Low Rank Matrix Factorization S3 Objects
Interpretive Structural Modelling (ISM). Returns a minimum-edge hierarchical digraph following J.N. Warfield's graph partitioning algorithm.
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
mfair: Matrix Factorization with Auxiliary Information in R
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
Computational Methods for Numerical Analysis
R Package: Regularized Principal Component Analysis for Spatial Data
🍱 R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow)
Add a description, image, and links to the matrix-factorization topic page so that developers can more easily learn about it.
To associate your repository with the matrix-factorization topic, visit your repo's landing page and select "manage topics."