期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
页码：416-426
出版社：Science and Information Society (SAI)
摘要：Multiclass classification based on unlabeled images
using computer vision and image processing is currently an
important issue. In this research, we focused on the phenomena
of constructing high-level features detector for class-driven
unlabeled data. We proposed a normalized restricted Boltzmann
machine (NRBM) to form a robust network model. The proposed
NRBM is developed to achieve the goal of dimensionality reduction
and provide better feature extraction with enhancement in
learning more appropriate features of the data. For increment
in learning convergence rate and reduction in complexity of
the NRBM, we add Polyak Averaging method when training
update parameters. We train the proposed NRBM network model
on five variants of Modified National Institute of Standards
and Technology database (MNIST) benchmark dataset. The
conducted experiments showed that the proposed NRBM is more
robust to noisy data as compared to state-of-art approaches.
关键词：Multiclass classification; restricted Boltzmann machine; Polyak averaging; image classification; Modified National
Institute of Standards and Technology Datasets
