It is also desirable to have a method that can simultaneously include cells from all batches in the analysis. Here we present DESC, an unsupervised deep learning algorithm that iteratively learns cluster-specifi
首先通过deep AE提取原始数据的低维表示,为了能够保留局部结构信息,加入了局部信息保护约束,此外又加入了组稀疏约束来对角化表示(representation)的吸引力,则加上重构误差,三个loss一起优化网络的聚类表示。训练完毕后需要使用k-means得到聚类 局部信息保护约束: E_{g}=\sum_{i,j\in k(i)}S_{ij}||f(x_{i...
三.VAE-Based Deep Clustering AE based和CDNN based方法相比于传统的聚类算法都有显著的提升,但是它们都是为了聚类专门设计的算法,而并没有去注意data的真实潜在结构,这让它们很难扩展到其他任务,例如:样本生成等,除此之外,维度缩减技术的潜在假设与聚类技术的潜在假设是相互独立的,因此没有理论能够证明网络能够学到...
Here, the authors develop SCALE, a deep learning method that leverages latent feature extraction for various tasks of scATACseq data analysis. Lei Xiong , Kui Xu & Qiangfeng Cliff Zhang Article 18 July 2019 | Open Access Single-cell reconstruction of follicular remodeling in the human adult...
v. Deep Learning Clustering Deep learning clustering methods use deep neural networks to learn clustering representations (Min et al., 2018). The optimizing objective of the deep clustering usually refers to as the loss function, has two parts: the clustering loss Lc and the network loss Ln. ...
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View ClusteringHCLS_CGLCVPR 2023- Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype AlignmentIMVCCVPR 2023- On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view...
Clustering Result In subject area: Computer Science A 'Clustering Result' is the outcome of grouping entities based on a similarity measure in unsupervised learning tasks. The result is dependent on the chosen similarity notion, such as distance metrics like squared Euclidean distance, and can be ...
machine-learningdata-miningdeep-learningclusteringsurveysrepresentation-learningdata-mining-algorithmsnetwork-embeddinggraph-convolutional-networksgcngraph-embeddinggraph-neural-networksself-supervised-learningdeep-clusteringgraphclustering UpdatedMay 6, 2025 Python ...
Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original ...
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