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Neural Network Training Through Matrix Factorization
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AI
DOI：10.1007/978-3-032-03705-3_3.png)
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Abstract
En 中文
In this paper, we present a novel supervised learning algorithm for neural network training based on QR decomposition with Householder reflections. The core of our study outlines the fundamental mathematical principles underlying this approach. To validate its effectiveness and robustness, we provide a detailed analysis of benchmark experiments, demonstrating the advantages of our method.
Keywords:
Neural Network Training
Matrix Factorization
QR Decomposition
Householder Reflections
Supervised Learning
Journal
A
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Papers:
24
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