Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning
| dc.contributor.author | Boongasame, Laor | |
| dc.contributor.author | Muangprathub, Jirapond | |
| dc.contributor.author | Thammarak, Karanrat | |
| dc.date.accessioned | 2026-08-06T10:51:40Z | |
| dc.date.available | 2026-08-06T10:51:40Z | |
| dc.date.issued | 2025-07-01 | |
| dc.description.abstract | This paper presents Laor Initialization, an innovative weight initialization technique for deep neural networks that utilizes forward-pass error feedback in conjunction with k-means clustering to optimize the initial weights. In contrast to traditional methods, Laor adopts a data-driven approach that enhances convergence’s stability and efficiency. The method was assessed using various datasets, including a gold price time series, MNIST, and CIFAR-10 across the CNN and LSTM architectures. The results indicate that the Laor Initialization achieved the lowest K-fold cross-validation RMSE (0.00686), surpassing Xavier, He, and Random. Laor demonstrated a high convergence success (final RMSE = 0.00822) and the narrowest interquartile range (IQR), indicating superior stability. Gradient analysis confirmed Laor’s robustness, achieving the lowest coefficients of variation (CV = 0.2230 for MNIST, 0.3448 for CIFAR-10, and 0.5997 for gold price) with zero vanishing layers in the CNNs. Laor achieved a 24% reduction in CPU training time for the Gold price data and the fastest runtime on MNIST (340.69 s), while maintaining efficiency on CIFAR-10 (317.30 s). It performed optimally with a batch size of 32 and a learning rate between 0.001 and 0.01. These findings establish Laor as a robust alternative to conventional methods, suitable for moderately deep architectures. Future research should focus on dynamic variance scaling and adaptive clustering. | |
| dc.identifier.citation | Big Data and Cognitive Computing, 9(7), 2025 | |
| dc.identifier.doi | 10.3390/bdcc9070181 | |
| dc.identifier.issn | 25042289 | |
| dc.identifier.other | 2-s2.0-105011413083 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17110 | |
| dc.source | Big Data and Cognitive Computing | |
| dc.subject | deep learning | |
| dc.subject | k-means clustering | |
| dc.subject | Laor Initialization | |
| dc.subject | neural network | |
| dc.subject | weight initialization | |
| dc.title | Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning | |
| dc.type | Article |