The novel contributions in this work are:A new ground motion clustering algorithm is proposed, which integrates 1) a convolutional autoencoder to learn the latent features (low-dimensional underlying characteri
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 ...
Already in 2017, a dataset obtained with USRP N210 SDR has served to experimentally validate a Gaussian Mixture Model (GMM) based clustering algorithm to authenticate users in mission critical machine type communications [28]. Adaptive neural networks have been suggested as a method of achieving ...
Network embedding aims to represent vertices in the network with low-dimensional dense real number vectors, so that the attained vertices can acquire the ability of representation and inference in vector space. With the expansion of the scale of complex networks, how to make the high-dimensional n...
Nilashi, M.; Dalvi-Esfahani, M.; Roudbaraki, M.Z.; Ramayah, T.; Ibrahim, O. A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques.J. Soft Comput. Decis. Support Syst.2016,3, 5. [Google Scholar] ...
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...
Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and cluster...
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....
If \({n}_{i}\, <\, 2\), then the clustering coefficient of node \(i\) is considered as zero. Global reaching centrality The global reaching centrality (GRC) of a network is calculated to evaluate the overall importance of a node based on its ability to reach other nodes in the ...
Deep Temporal Clustering: Fully Unsupervised Learning of Time-Domain Features (Ph.D. thesis) Arizona State University (2018) Google Scholar [26] Ibidunmoye O., Rezaie A.-R., Elmroth E. Adaptive anomaly detection in performance metric streams IEEE Trans. Netw. Serv. Manag., 15 (1) (2017...