Unsupervised learning for dimensionality reduction

机译：无监督学习以减少维度

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This paper presents an unsupervised training algorithm for dimensionality reduction. The proposed algorithm is based on the separate learning of different layers of a neural network. We called such a method the sectioning or level by level learning. It permits to perform the best compression of the data set in which it minimizes the mean square error between the compressed data and the original data set. The proposed method is characterized by small training time. The viability of this approach is demonstrated on numerical experiments.

机译：本文提出了一种用于降维的无监督训练算法。所提出的算法基于神经网络不同层的单独学习。我们称这种方法为分段学习或逐级学习。它允许对数据集执行最佳压缩，从而最大程度地减少压缩数据和原始数据集之间的均方误差。该方法具有训练时间短的特点。数值实验证明了这种方法的可行性。

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来源
《Second ICSC Symposium on Engineering of Intelligent Systems, Jun 27-30, 2000, Scotland, U.K.》|2000年|p.140-144|共5页
会议地点 Scotland U.K.
作者
Vladimir Golovko; Oleg Ignatiuk; Yury Savitsky; T. Laopoulos; A. Sachenko; L. Grandinetti;
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作者单位

Brest Polytechnic Institute, Belarus;

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会议组织
原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
关键词
unsupervised training; data compression; gradient descent method;

机译：无监督的培训；数据压缩梯度下降法;

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Unsupervised learning for dimensionality reduction

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