Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of s
q= self.model.predict(x, verbose=0) p= self.target_distribution(q)#update the auxiliary target distribution p#evaluate the clustering performancey_pred = q.argmax(1)ifyisnotNone: acc= np.round(metrics.acc(y, y_pred), 5) nmi= np.round(metrics.nmi(y, y_pred), 5) ari= np.round(...
本文研究路线:深度自编码器(Deep Autoencoder)->Deep Embedded Clustering(DEC)->Improved Deep Embedded clustering(IDEC)->Deep Convolutional Embedded Clustering(DCEC)->Deep Fuzzy K-means(DFKM),其中Deep Autoencoder已经在深度自编码器(Deep Autoencoder)MATLAB解读中提到,也有很多深度自编码器的改进方法,不详细...
论文笔记:Improved Deep Embedded Clustering with Local Structure Preservation )在图像和文本数据集上的实验从经验上验证了局部结构保存的重要性和算法的有效性。 总之:IDEC可以联合执行聚类并学习具有局部结构保护的代表性特征。 2.网络框架聚类损失(Clustering... EmbeddedClusteringalgorithm(IDEC) 考虑到了保留数据结构...
Deep Embedded Clustering (DEC) [Xie et al., 2016] 算法以自学习的方式定义了一个有效的目标。定义的聚类损失用于同时更新变换网络和聚类中心的参数。集群分配隐式集成到软标签。然而,聚类损失不能保证局部结构的保存。因此,特征转换可能会被误导,导致嵌入空间的损坏。
ZINB model-based deep embedded clustering Clustering analysis is conducted on the embedded latent space27,28. LetXdenote a set ofncells with\(x_ i \in {\mathbb{N}}^d\)representing the read counts ofdgenes in theIth cell. scDCC applies the denoising ZINB model-based autoencoder to learn ...
Here, we have developed scDeepCluster, a single-cell model-based deep embedded clustering method, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Based on testing extensive simulated data and real datasets from four representative ...
Deep Nonparametric Clustering (DNC) 该算法使用无监督特征学习和DBN进行聚类分析,首先训练DBN将原始数据映射到特征编码,之后使用nonparametric maximum margin clustering (NMMC)算法得到训练数据的簇数量和label数量,最后fine tune DBN的top layer参数 Deep Embedded Clustering (DEC) 最经典模型之一,其使用AE作为网络框架...
DEC (Unsupervised Deep Embedding for Clustering Analysis) IDEC (Improved Deep Embedded Clustering with Local Structure Preservation) DCEC (Deep Clustering with Convolutional Autoencoders) DFKM (Deep Paper intensive reading (十六):Deep learning enables accurate clustering and batch effect removal Correlatio...
Deep Clustering Deep Clustering方法的思路很简单,它的学习过程如下: Repeat 用K-means算法对特征值进行聚类,产生分类用的伪标签 用K-means产生的伪标签训练Convnet网络 End 这种方法交替的学习数据的特征和对它们进行聚类。但如何保证这个系统的自举以及收敛?作者主要归咎于卷积网络的结构对图片有判别的先验:一个随机...