Weighted Deep Embedded Clustering for Robust Representation Learning in Noisy Data
A Reliability-Based Deep Embedded Clustering (RDEC) approach that improves clustering reliability through a novel distance-aware weighting strategy, coupled with a novel Kullback–Leibler divergence objective function to focus on the most representative data instances and guide the clustering process.