Robust deep graph based learning for binary classification

Ye, Minxiang and Stankovic, Vladimir and Stankovic, Lina and Cheung, Gene (2020) Robust deep graph based learning for binary classification. IEEE Transactions on Signal and Information Processing over Networks. ISSN 2373-7778 (In Press)

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    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.

    ORCID iDs

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