Most existing feature learning methods optimize inflexible handcrafted features and the affinity matrix is constructed by shallow linear embedding methods. Different from these conventional methods, we pretrain a generative neural network by stacking convolutional autoencoders to learn the latent data representation and then construct an affinity graph with them as a prior. Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss. The locality-preserving loss designed by the constructed affinity graph serves as prior to preserve the local structure during the fine-tuning stage, which in turn improves the quality of feature representation effectively. Furthermore, the self-expressive layer between the encoder and decoder is based on the assumption that each latent feature is a linear combination of other latent features, so the weighted combination coefficients of the self-expressive layer are used to construct a new refined affinity graph for representing the data structure. We conduct experiments on four datasets to demonstrate the superiority of the representation ability of our proposed model over the state-of-the-art methods.
python UDLL_graph_Coil20.py
python UDLL_Coil20.pyWe appreciate it if you cite the following paper:
@Article{Zhan8052206,
author = {Changlu Chen and Chaoxi Niu and Xia Zhan and Kun Zhan},
title = {A Generative Approach to Unsupervised Deep Local Learning},
journal = {Journal of Electronic Imaging},
year = {2019},
volume = {28},
number = {3},
pages = {},
}
If you have any questions, feel free to contact me. (Email: ice.echo#gmail.com)