Robust deep graph based learning for binary classification

Minxiang Ye, Vladimir Stankovic, Lina Stankovic, Gene Cheung

Research output: Contribution to journalArticlepeer-review


Convolutional neural network (CNN)-based feature learning has become the state-of-the-art for many applications since, given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks.
However, feature learning is more challenging if training labels are noisy as CNN tends to overfit to the noisy training labels, resulting in sub-par classification performance. In this paper, we propose a robust binary classifier by learning CNN-based deep metric functions, to construct a graph, used to clean the noisy labels via graph Laplacian regularization (GLR). The denoised labels are then used in two proposed loss correction functions to regularize the deep metric functions. As a result, the nodetonode correlations in the graph are better reflected, leading to improved predictive performance. The experiments on three datasets, varying in number and type of features and under different levels of noise, demonstrate that given a noisy training dataset for the semi-supervised classification task, our proposed networks outperform several state-of-the-art classifiers, including label-noise robust support vector machine, CNNs with three different robust loss functions, model-based GLR, and dynamic graph CNN classifiers.
Original languageEnglish
Number of pages13
JournalIEEE Transactions on Signal and Information Processing over Networks
Publication statusAccepted/In press - 12 Nov 2020


  • deep learning
  • graph Laplacian regularization
  • semi-supervised learning
  • binary classification

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