TL;DR
An End-to-end Sample-Quality-Adaptive Contrastive Fusion for Deep Multi-View Clustering (SACC) framework, which dynamically adjusts the contrastive learning intensity according to sample-wise cross-view inconsistency calculated via squared Euclidean distance discrepancy and cosine similarity difference is proposed.
This work proposes a Self-supervised Probability Distribution-driven Deep Incomplete Multi-view Clustering method (SPDIM), which first handles missing views at the data level, utilizing uncertainty-aware sampling to interpolate missing information and thereby enforce global consistency across samples.
Multi-view sequential data are widely used in the real world, while representation learning can integrate heterogeneous data from different views to improve learning performance which makes significant progress in recent years. However, most existing algorithms assume that sequential data are complete, which frequently...
IASR is proposed, an Instance-Aligned Semantic Reconstruction framework for IMVC that formulates missing-view recovery as a cross-view token alignment generator, and introduces a stable–active dual-encoder representation architecture with a generative-adaptive contrastive learning strategy that tightly couples view com...
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