Linear feature transform and enhancement of classification on deep neural network

DOI10.1007/s10915-018-0666-1zbMath1484.68210OpenAlexW2791913165MaRDI QIDQ1785494

Yingyong Qi, Penghang Yin, Jack X. Xin

Publication date: 28 September 2018

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10915-018-0666-1

zbMATH Keywords

difference of weighted nuclear normsenhanced classificationlinear feature transformlow-dimensional features

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07) Convex programming (90C25) Linear transformations, semilinear transformations (15A04)

Related Items (3)

DC programming and DCA: thirty years of developments ⋮ Global convergence and geometric characterization of slow to fast weight evolution in neural network training for classifying linearly non-separable data ⋮ Learning quantized neural nets by coarse gradient method for nonlinear classification

Uses Software

• Jellyfish
• Caffe
• CIFAR
• ImageNet
• cuda-convnet
• AlexNet

Cites Work

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• Characterization of the subdifferential of some matrix norms
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• Automatic speech recognition. A deep learning approach
• Parallel stochastic gradient algorithms for large-scale matrix completion
• Deep Learning: Methods and Applications
• A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem
• Iterative <em>l</em>₁ Minimization for Non-Convex Compressed Sensing

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