KPHD-Net replaces KL divergence with Proper Hölder Divergence in evidential multi-view learning, adding Kalman-filtered Dempster-Shafer fusion to quantify uncertainty in classification and clustering.
On the Dempster-Shafer framework and new combination rules,
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Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures
KPHD-Net replaces KL divergence with Proper Hölder Divergence in evidential multi-view learning, adding Kalman-filtered Dempster-Shafer fusion to quantify uncertainty in classification and clustering.