scKDGM proposes a KAN-guided dynamic graph masked learning framework with GDP-Mask, TAKGCN encoder, mask-guided recovery, cross-view contrastive learning and ZINB loss that outperforms 10 baselines on 12 scRNA-seq datasets in NMI and ARI.
Non-gradient hash factor learning for high-dimensional and incomplete data representation learning,
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EKL integrates Extended Kalman Filter for temporal latent features and alternating least squares for time-invariant features to predict missing temporal QoS data with claimed gains in accuracy and efficiency.
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scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering
scKDGM proposes a KAN-guided dynamic graph masked learning framework with GDP-Mask, TAKGCN encoder, mask-guided recovery, cross-view contrastive learning and ZINB loss that outperforms 10 baselines on 12 scRNA-seq datasets in NMI and ARI.
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A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis
EKL integrates Extended Kalman Filter for temporal latent features and alternating least squares for time-invariant features to predict missing temporal QoS data with claimed gains in accuracy and efficiency.