Abstract
Many real-world tensors come with missing values. The task of estimation of such missing elements is called tensor completion (TC). It is a fundamental problem with a wide range of applications in data mining, machine learning, signal processing, and computer vision. In the last decade, several different algorithms have been developed, couple of them have shown high-quality performance in diverse domains. However, our investigation shows that even state-of-the-art TC algorithms sometimes make poor estimations for few cases that are not noticeable if we look at their overall performance. However, such wrong estimates might have a severe effect on some decisions. It becomes a crucial issue in applications where humans are involved. Making bad decisions based on such poor estimations can harm fairness. We propose the first algorithm for tensor completion post-correction, called TCPC, to identify some of such poor estimates from the output of any TC algorithm and refine them with more realistic estimations. Our initial experiments with five real-life tensor datasets show that TCPC is an effective post-correction method.
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Fanaee-T, H. (2022). Tensor Completion Post-Correction. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham. https://doi.org/10.1007/978-3-031-01333-1_8
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DOI: https://doi.org/10.1007/978-3-031-01333-1_8
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