Multi-view learning for object classification

A unifying framework for vector-valued manifold regularization and multi-view learning
H. Q. Minh, L. Bazzani, V. Murino
The 30th International Conference on Machine Learning (ICML), 2013
MVL code / bibtex
@inproceedings{Minh:ICML13,
  title = {A unifying framework for vector-valued manifold
           regularization and multi-view learning},
  author = {Minh, H. Q. and Bazzani, L. and Murino, V.},
  booktitle = {Proceedings of the 30th International Conference
               on Machine Learning (ICML-13)},
  year = {2013},
  editor = {Sanjoy Dasgupta and David Mcallester},
  month = may,
  number = {2},
  pages = {100-108},
  publisher = {JMLR Workshop and Conference Proceedings},
  volume = {28}
}
    

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
H. Q. Minh, L. Bazzani, V. Murino
Journal of Machine Learning Research (JMLR), 2016
arXiv / bibtex
@article{Minh:JMLR16,
  author  = {H{{\`a}} Quang Minh and Loris Bazzani and Vittorio Murino},
  title   = {A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for 
  Manifold Regularization and Co-Regularized Multi-view Learning},
  journal = {Journal of Machine Learning Research},
  year    = {2016},
  volume  = {17},
  number  = {25},
  pages   = {1-72},
  url     = {http://jmlr.org/papers/v17/14-036.html}
}

Details

We propose a general vector-valued reproducing kernel Hilbert spaces formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting. In the case of least square loss function, we provide a closed form solution with an efficient implementation. Numerical experiments on challenging multi-class categorization problems show that our multi-view learning formulation achieves results which are comparable with state of the art and are significantly better than single-view learning.

multiview_pic

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(link to github)

See the instructions in the README.md file.

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