/multi-view-learning

Paper list and resources on multi-view learning.

multi-view learning

Resources on multi-view learning (MVL). This web have been merged to the new web.

Survey

  1. A Survey on Multi-view Learning

    • Chang Xu, Dacheng Tao, Chao Xu 2013
    • Paper

  2. Multi-view learning overview: Recent progress and new challenges

    • Jing Zhao,Xijiong Xie, Xin Xu, Shiliang Sun 2017
    • Paper

Tutorial

  1. Multiview Feature Learning Tutorial @ CVPR 2012

  2. Multiview Feature Learning @ IPAM 2012

MVL with Deep Learning

  1. On deep multi-view representation learning

    • Wang, Weiran, et al. 2015.

  2. Multi-view deep network for cross-view classification

    • Kan, Meina, Shiguang Shan, and Xilin Chen 2016.

  3. Multi-view perceptron: a deep model for learning face identity and view representations

    • Zhu, Zhenyao, et al. 2014.

  4. A multi-view deep learning approach for cross domain user modeling in recommendation systems

    • Elkahky, Ali Mamdouh, Yang Song, and Xiaodong He 2015.

  5. A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning

    • Yuan, Ye, et al. 2018.

  6. Volumetric and multi-view cnns for object classification on 3d data

    • Qi, Charles R., et al. 2016.

Multimodal Deep Learning

  1. Multimodal deep learning

    • Ngiam, Jiquan, et al. ICML 2011.
    • paper

  2. Multimodal learning with deep boltzmann machines

    • Srivastava, Nitish, and Ruslan R. Salakhutdinov NIPS 2012
    • paper

  3. Deep multimodal learning: A survey on recent advances and trends

    • Ramachandram, Dhanesh, and Graham W. Taylor 2017.

Brain Image

  1. Deep Learning Approaches to Unimodal and Multimodal Analysis of Brain Imaging Data With Applications to Mental Illness

    • Calhoun, Vince, and Sergey Plis 2018.

  2. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease

    • Shi, Jun, et al. 2018.

  3. Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection

    • Liu L , Wang Q , Adeli E , et al. 2018.
    • Discussed in lab meeting (LJ Cao).