Deep graph regularized learning for binary classification

Ye, Minxiang and Stankovic, Vladimir and Stankovic, Lina and Cheung, Gene (2019) Deep graph regularized learning for binary classification. In: 2019 International Conference on Acoustics, Speech, and Signal Processing, 2019-05-12 - 2019-05-17.

[thumbnail of Ye-etal-ICASSP-2019-Deep-graph-regularized-learning-for-binary-classification]
Text (Ye-etal-ICASSP-2019-Deep-graph-regularized-learning-for-binary-classification)
Accepted Author Manuscript

Download (223kB)| Preview


    With growing interest in data-driven classification, deep learning is now prevalent thanks to its ability to learn feature mapping functions solely from data. For very small training sets, however, deep learning, even with traditional regularization techniques, often overfits, resulting in sub-par classification performance. In this paper, we propose a novel binary classifier deep learning method, based on an iterative quadratic programming (QP) formulation with a graph Laplacian regularizer (GLR), combining the merits of model-based and data-driven approaches. Specifically, the proposed network employs a convolutional neural network (CNN) to learn deep features, which are used to define edge weights for a graph to pose a convex QP problem. Further, we design a novel loss function to penalize samples at the class boundary during semi-supervised learning. Results demonstrate that given a small-size training dataset, our network outperforms several state-of-the-art classifiers, including CNN, model-based GLR, and dynamic graph CNN classifiers.

    ORCID iDs

    Ye, Minxiang ORCID logoORCID:, Stankovic, Vladimir ORCID logoORCID:, Stankovic, Lina ORCID logoORCID: and Cheung, Gene;