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.
A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Introduces a core-conditioned regularized tri-factorization framework for low-rank approximation that jointly manages accuracy, factor scale, and numerical conditioning with supporting analysis and validation.
citing papers explorer
-
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.
-
Core-Conditioned Regularized Matrix Tri-Factorization for High-Dimensional Structured Systems
Introduces a core-conditioned regularized tri-factorization framework for low-rank approximation that jointly manages accuracy, factor scale, and numerical conditioning with supporting analysis and validation.