Group sparse autoencoder
作者：
Highlights：
• Group Sparse AutoEncoder (GSAE) learns better discriminative features compared to an unsupervised autoencoder.
• Class label based ℓ2,1-regularization is incorporated to squared error reconstruction loss function using a majorization-minimization approach.
• The proposed GSAE is used to learn minutia representation from noisy latent fingerprint images.
• Results on standard image datasets, MNIST, CIFAR-10, and SVHN and latent fingerprint image datasets, NIST SD-27 and MOLF, show effectiveness of the proposed GSAE feature extraction approach.
摘要
•Group Sparse AutoEncoder (GSAE) learns better discriminative features compared to an unsupervised autoencoder.•Class label based ℓ2,1-regularization is incorporated to squared error reconstruction loss function using a majorization-minimization approach.•The proposed GSAE is used to learn minutia representation from noisy latent fingerprint images.•Results on standard image datasets, MNIST, CIFAR-10, and SVHN and latent fingerprint image datasets, NIST SD-27 and MOLF, show effectiveness of the proposed GSAE feature extraction approach.
论文关键词：Supervised autoencoder,Group sparsity,Latent fingerprint,Minutia extraction
论文评审过程：Received 22 April 2016, Revised 3 January 2017, Accepted 12 January 2017, Available online 18 January 2017, Version of Record 22 March 2017.
论文官网地址：https://doi.org/10.1016/j.imavis.2017.01.005
