کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7561914 | 1491502 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Noisy matrix completion on a novel neural network framework
ترجمه فارسی عنوان
اتمام ماتریس پر سر و صدا در یک چارچوب شبکه جدید عصبی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
A novel matrix completion algorithm based on the iterative application of neural networks is presented. It is shown that Bayesian regularization provides proper protection against overfitting, more so than early-stopping or a combination of both. The flexibility to increase the size of the hidden layer provides a better description of increasingly nonlinear relationships between the known and missing values in the data with a limited loss in generalization ability. The proposed neural network algorithm provides a more accurate estimation of missing values than current matrix completion algorithms based on iterative regression approaches or PCA applications for many datasets with fractions of missing values from 5 to 40%. The neural network algorithm performs particularly well on datasets where the number of observations significantly exceeds the number of features.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 177, 15 June 2018, Pages 1-7
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 177, 15 June 2018, Pages 1-7
نویسندگان
Samuel Mercier, Ismail Uysal,