TR2017-018

Disc-Glasso: Discriminative Graph Learning with Sparsity Regularization


    •  Kao, J.-Y., Tian, D., Mansour, H., Ortega, A., Vetro, A., "Disc-Glasso: Discriminative Graph Learning with Sparsity Regularization", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017.
      BibTeX TR2017-018 PDF
      • @inproceedings{Kao2017mar,
      • author = {Kao, Jiun-Yu and Tian, Dong and Mansour, Hassan and Ortega, Antonio and Vetro, Anthony},
      • title = {Disc-Glasso: Discriminative Graph Learning with Sparsity Regularization},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2017,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2017-018}
      • }
  • MERL Contacts:
  • Research Area:

    Digital Video

Abstract:

Learning graph topology from data is challenging. Previous work leads to learning graphs on which the graph signals used for training are smooth. In this paper, we propose an optimization framework for learning multiple graphs, each associated to a class of signals, such that representation of signals within a class and discrimination of signals in different classes are both taken into consideration. A Fisher-LDA-like term is included in the optimization objective function in addition to the conventional Gaussian ML objective. A block coordinate descent algorithm is then developed to estimate optimal graphs for different categories of signals, which are then used to efficiently classify the different signals. Experiments on synthetic data demonstrate that our proposed method can achieve better discrimination between the learned graphs, leading to improvements in subsequent classification tasks.

 

  • Related News & Events

    •  NEWS   MERL to present 10 papers at ICASSP 2017
      Date: March 5, 2017 - March 9, 2017
      Where: New Orleans
      MERL Contacts: Petros T. Boufounos; Takaaki Hori; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Anthony Vetro; Ye Wang
      Research Areas: Computer Vision, Computational Sensing, Digital Video, Information Security, Speech & Audio
      Brief
      • MERL researchers will presented 10 papers at the upcoming IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), to be held in New Orleans from March 5-9, 2017. Topics to be presented include recent advances in speech recognition and audio processing; graph signal processing; computational imaging; and privacy-preserving data analysis.

        ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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