A Comparison between Nonnegative Matrix Factorization and Feature Selection for Machine Learning of Personalized Recommender System

2020 ◽  
Vol 21 (4) ◽  
pp. 793-798
Author(s):  
Chan-Woo Yoo ◽  
Hee-Chern Kim
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Recommender System ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Selection For ◽  
Personalized Recommender System

Improved Nonnegative Matrix Factorization Based Feature Selection for High Dimensional Data Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 2344-2348
Author(s):  
Lin Cheng Jiang ◽  
Wen Tang Tan ◽  
Zhen Wen Wang ◽  
Feng Jing Yin ◽  
Bin Ge ◽  
...  
Keyword(s):  
Feature Selection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
High Dimensional Data ◽  
Nonnegative Matrix ◽  
High Dimensional ◽  
Feature Subset ◽  
Feature Extraction Method ◽  
Optimal Feature Subset ◽  
Optimal Feature

Feature selection has become the focus of research areas of applications with high dimensional data. Nonnegative matrix factorization (NMF) is a good method for dimensionality reduction but it cant select the optimal feature subset for its a feature extraction method. In this paper, a two-step strategy method based on improved NMF is proposed.The first step is to get the basis of each catagory in the dataset by NMF. Added constrains can guarantee these basises are sparse and mostly distinguish from each other which can contribute to classfication. An auxiliary function is used to prove the algorithm convergent.The classic ReliefF algorithm is used to weight each feature by all the basis vectors and choose the optimal feature subset in the second step.The experimental results revealed that the proposed method can select a representive and relevant feature subset which is effective in improving the performance of the classifier.

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