However, due to the high dimensionality of the input feature values, the data being fed to clustering algorithms usually contains noise and thus could lead to in-accurate clustering results. While traditional dimension reduction and feature selection algorithms could be used to address this problem, ...
By constructing a hashing layer as a hidden layer of the autoencoder, hash learning is performed together with unsupervised clustering by minimizing the overall loss. AUCH can unify unsupervised clustering and retrieval tasks into a single learning model. In addition, the method can use a deep ...
Diallo B, Hu J, Li T, et al. Deep embedding clustering based on contractive autoencoder[J]. Neurocomputing, 2021, 433: 96-107. 摘要导读 作者指出现有的聚类算法在表示学习上还很欠缺。本文通过同时从原始数据中保留重要信息,和将原始样本和其对应的增强表示约束到一起来实现对表示的学习。此外,为保证...
In the proposed Representation Learning for Clustering-based Classification (RLCC), the new representation of each cluster is generated locally to increase the performance of OCCs in term of accuracy. This method still preserves the scalability property as one of the significant advantages of CC ...
Graph embedding clustering often combines deep autoencoder and graph clustering algorithms, which can learn the latent compact representation to explore both the rich content and structural information. 模型浅析 模型的结构顺序如下: 首先是对数据的预处理 ...
When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability....
1. Pattern recognition and clustering Pattern recognition is a mature field in computer science with well-established techniques for the assignment of unknown patterns to categories, or classes. Apatternis defined as a vector of some number of measurements, calledfeatures.Usually, a pattern recognition...
(under-parameterized) autoencoder models leads to inconsistent spatial clusters across mouse samples: cells from the same cluster (blue) correspond to different regions in 13-months control (left) and 13-months AD (right) mice. The latent dimension of this model is 1024.dClustering of the ...
When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability. 展开 ...
Autoencoder-based feature construction for IoT attacks clustering Variations in commands executed as part of the attack process can be used to determine the behavioural patterns of IoT attacks. Existing approaches rely on... J Haseeb,M Mansoori,Y Hirose,... - 《Future Generations Computer Systems...