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MvDSCN

:game_die: Tensorflow Repo for "Multi-view Deep Subspace Clustering Networks"

[Paper] (submitted to TIP 2019)

Overview

In this work, we propose a novel multi-view deep subspace clustering network (MvDSCN) by learning a multi-view self-representation matrix in an end to end manner. MvDSCN consists of two sub-networks, i.e., diversity network (Dnet) and universality network (Unet). A latent space is built upon deep convolutional auto-encoders and a self-representation matrix is learned in the latent space using a fully connected layer. Dnet learns view-specific self-representation matrices while Unet learns a common self-representation matrix for all views. To exploit the complementarity of multi-view representations, Hilbert Schmidt Independence Criterion (HSIC) is introduced as a diversity regularization, which can capture the non-linear and high-order inter-view relations. As different views share the same label space, the self-representation matrices of each view are aligned to the common one by a universality regularization.

Requirements

• Tensorflow
• scipy
• numpy
• sklearn
• munkres

Usage

• Test by Released Result:

python main.py --test

• Train Network with Finetune.

We have released the pretrain model in /pretrain folder, you can train it with finetune:

python main.py --ft

• Pretrain Auoencoder From Scratch:

You re-train autoencoder from scarath:

python main.py

Citation

Please star :star2: this repo and cite :page_facing_up: this paper if you want to use it in your work.

@article{zhu2019multiview,
    title={Multi-view Deep Subspace Clustering Networks},
    author={Pengfei Zhu and Binyuan Hui and Changqing Zhang and Dawei Du and Longyin Wen and Qinghua Hu},
    journal={ArXiv: 1908.01978}
    year={2019}
}

License

MIT License