Linear Feature Transform and Enhancement of Classification on Deep Neural Network

Penghang Yin University of California at Irvine Jack Xin University of California at Irvine Yingyong Qi University of California at Irvine

Information Theory mathscidoc:1608.19003

2016
A weighted and convex regularized nuclear norm model is introduced to construct a rank constrained linear transform on feature vectors of deep neural networks. The feature vectors of each class are modeled by a subspace, and the linear transform aims to enlarge the pairwise angles of the subspaces. The weight and convex regularization resolve the rank degeneracy of the linear transform. The model is computed by a di erence of convex function algorithm whose descent and convergence properties are analyzed. Numerical experiments are carried out in convolutional neural networks on CAFFE platform for 10 class handwritten digit images (MNIST) and small object color images (CIFAR-10) in the public domain. The transformed feature vectors improve the accuracy of the network in the regime of low dimensional features subsequent to principal component analysis. The feature transform is independent of the network structure, and can be applied without retraining the feature extraction layers of the network.
Linear feature transform, di erence of weighted nuclear norms, deep neural networks, enhanced classification
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@inproceedings{penghang2016linear,
  title={Linear Feature Transform and Enhancement of Classification on Deep Neural Network},
  author={Penghang Yin, Jack Xin, and Yingyong Qi},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160818153312294547247},
  year={2016},
}
Penghang Yin, Jack Xin, and Yingyong Qi. Linear Feature Transform and Enhancement of Classification on Deep Neural Network. 2016. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160818153312294547247.
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