Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises
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DOI: 10.1109/tpami.2018.2794470
Abstract: Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in… read more here.
Keywords: semi supervised; outliers feature; feature noises; sample outliers ... See more keywords