A point interpolation algorithm resulting from weighted linear regression

2021 ◽  
Vol 50 ◽  
pp. 101304
Author(s):  
Leonardo Ramos Emmendorfer ◽  
Graçaliz Pereira Dimuro
Keyword(s):  
Linear Regression ◽  
Interpolation Algorithm ◽  
Weighted Linear Regression ◽  
Point Interpolation

An Improved Instance Weighted Linear Regression

2010 ◽  
Vol 5 (3) ◽  
pp. 122-128
Author(s):  
Chaoqun Li ◽  
Hongwei Li
Keyword(s):  
Linear Regression ◽  
Weighted Linear Regression

Database Management Method Based on Strength of Nonlinearity for Locally Weighted Linear Regression

2019 ◽  
Vol 52 (6) ◽  
pp. 554-561 ◽  
Author(s):  
Sanghong Kim ◽  
Kazuki Mishima ◽  
Manabu Kano ◽  
Shinji Hasebe
Keyword(s):  
Linear Regression ◽  
Database Management ◽  
Weighted Linear Regression ◽  
Management Method

Fast reconstruction of Raman spectra based on global weighted linear regression

2020 ◽  
Vol 203 ◽  
pp. 104073
Author(s):  
Xian-guang Fan ◽  
Long Liu ◽  
Zhe-ming Kang ◽  
Ying-jie Zeng ◽  
Yu-liang Zhi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Raman Spectra ◽  
Weighted Linear Regression ◽  
Fast Reconstruction

scholarly journals A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xu Yu ◽  
Jun-yu Lin ◽  
Feng Jiang ◽  
Jun-wei Du ◽  
Ji-zhong Han
Keyword(s):  
Linear Regression ◽  
Collaborative Filtering ◽  
Feature Construction ◽  
Target Domain ◽  
Regression Problem ◽  
Filtering Algorithm ◽  
Weighted Linear Regression ◽  
Cross Domain ◽  
Collaborative Filtering Algorithm ◽  
Sparsity Problem

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

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