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On Feature Extraction Capabilities of Fast Orthogonal Neural Networks

机译:快速正交神经网络的特征提取能力研究

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The paper investigates capabilities of fast orthogonal neural networks in a feature extraction task for classification problems. Neural networks with an architecture based on the fast cosine transform, type II and IV are built and applied for extraction of features used as a classification base for a multilayer perceptron. The results of the tests show that adaptation of the neural network allows to obtain a better transform in the feature extraction sense as compared to the fast cosine transform. The neural implementation of both the feature extractor and the classifier enables integration and joint learning of both blocks.
机译:本文研究了快速正交神经网络在分类问题的特征提取任务中的功能。建立了具有基于快速余弦变换(II型和IV型)的体系结构的神经网络,并将其用于提取用作多层感知器的分类基础的特征。测试结果表明,与快速余弦变换相比,神经网络的自适应可以在特征提取方面获得更好的变换。特征提取器和分类器的神经实现方式使这两个块可以集成和联合学习。

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