Deep Positive Unlabeled Learning with a Sequential Bias
For many domains, from video stream analytics to human activity recognition, only weakly-labeled datasets are available. Worse yet, the given labels are often assigned sequentially, resulting in sequential bias. Current Positive Unlabeled (PU) classifiers, a state-of-the-art family of robust semi-supervised methods, are ineffective under sequential bias. In this work, we propose DeepSPU, the first method to address this sequential bias problem. DeepSPU tackles the two interdependent subproblems of learning both the latent labeling process and the true class likelihoods within one architecture. We achieve this by developing a novel iterative learning strategy aided by theoretically-justified cost terms to avoid collapsing into a naive classifier. Our experimental studies demonstrate that DeepSPU outperforms state-of-the-art methods by over 10% on diverse real-world datasets.
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