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Collections for state-of-the-art and novel deep neural network-based multi-view clustering approaches (papers & codes). According to the integrity of multi-view data, such methods can be further subdivided into Deep Multi-view Clustering(DMVC) and Deep Incomplete Multi-view Clustering(DIMVC).We...
A novel approach was proposed in this study, termed deep Grassmannian multiview subspace clustering with contrastive learning (DGMVCL). The proposed algorithm initially utilized a feature extraction module (FEM) to map the original input samples into a feature subspace. Subsequently, the manifold...
Following this, we enable visualizing trajectory-specific gene expression trends and cascades of gene activation14, clustering expression trends14 or arranging cells in a circular embedding14,74 to summarize fate probabilities. We provide the necessary tools for each step of the downstream analysis as ...
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point informat
Incomplete Multi-view Clustering via Subspace Learning Unified subspace learning for incomplete and... Learning CIKM’15 Unified subspace learning for incomplete and unlabeled multi-view data Incomplete 模型压缩备用 categorized into four schemes: parameter pruning and sharing low-rank factorization transferr...
Graph learning for multiview clustering Most existing graph-based clustering methods need a predefined graph and their clustering performance highly depends on the quality of the graph. Aiming to improve the multiview clustering performance, a graph learning-based method is proposed to improve the ...
Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the data ...
For the clustering task, we detected molecular clusters that differed in their 10-year survival rates for breast cancer. For the reconstruction task, we were able to reconstruct handwritten images using a few pixels while achieving competitive classification accuracy. The results of our real data ...
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