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A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  sparse filtering.  ...  One2Multi Graph Autoencoder for Multi-view Graph Clustering (O2MAC) [69] deals with the multi-view graph by dividing it into multiple one-view graphs and assigning each with an AE, named One2Multi.  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

A Survey of Applied Machine Learning Techniques for Optical OFDM based Networks [article]

Hichem Mrabet, Elias Giaccoumidis, Iyad Dayoub
2021 arXiv   pre-print
In essence, these ML techniques could be beneficial for any multi-carrier approach (e.g. filter bank modulation).  ...  We indicate the strict conditions under which a ML algorithm should perform classification, regression or clustering.  ...  Unsupervised ML algorithms for clustering In the-state-of-the-art, various clustering algorithms are used in optical networks, such as K-means [55, 64, 70] , sparse K-means [91] , fuzzy-logic C-means  ... 
arXiv:2105.03289v1 fatcat:wfmal7ntnvhf5e2qmdeid7takq

Edge computing server placement with capacitated location allocation

Tero Lähderanta, Teemu Leppänen, Leena Ruha, Lauri Lovén, Erkki Harjula, Mika Ylianttila, Jukka Riekki, Mikko J. Sillanpää
2021 Journal of Parallel and Distributed Computing  
Evaluations are performed with a data set collected in a realworld network, consisting of both dense and sparse deployments of access points across a city area.  ...  We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing.  ...  , Finland and the Technology Industries of Finland  ... 
doi:10.1016/j.jpdc.2021.03.007 fatcat:azjyi436kfaetess6hcmvbl5ey

Edge computing server placement with capacitated location allocation [article]

Tero Lähderanta, Teemu Leppänen, Leena Ruha, Lauri Lovén, Erkki Harjula, Mika Ylianttila, Jukka Riekki, Mikko J. Sillanpää
2020 arXiv   pre-print
The algorithm is evaluated in two distinct scenarios, with high capacity servers for edge computing in general and low capacity servers for Fog computing.  ...  In the evaluation, we utilize a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area.  ...  and the Technology Industries of Finland Centennial Foundation, by Academy of Finland Profi 5 funding for mathematics and AI: data insight for high-dimensional dynamics, and the personal grant for Lauri  ... 
arXiv:1907.07349v2 fatcat:ctsc7jfqzbdb7krb2eetuh6t6y

Evolving fuzzy neural networks in adaptive knowledge bases to support task-oriented decision making for sensor management

Fook Wai Kong, Gee Wah Ng, Yuan Sin Tan, Chung Huat Tan
2007 2007 10th International Conference on Information Fusion  
The two algorithmic contributions consist of modifications of EFUNN's original learning mechanism to handle training records with outlying inputs and those with contradictory class outputs that characterise  ...  To assist the human operators in dealing better with the intense pressure to perform effectively in such environments, adaptive knowledge bases capable of capturing human operators' behavioural patterns  ...  The unsupervised clustering attempts to tackle sparse training data and both the unsupervised and supervised learning require only single pass for the weights to be updated.  ... 
doi:10.1109/icif.2007.4408088 dblp:conf/fusion/KongNTT07 fatcat:4m7gn6z6gvcuvkcy4efjinn7tq

Fuzzy V(variation)-level clustering

J.M. Lakshmi, G. Raju
2017 International Journal of Advanced Intelligence Paradigms  
Experiments were done with both deterministic crisp algorithms, and fuzzy algorithms and the results were compared to observe the efficiency of the model to form soft clusters. Kerala.  ...  His areas of research interest cover image processing, data mining and high performance computing, with Fuzzy V(variation)-level clustering 33 many publications.  ...  Mathematically fuzziness means multi valued or multi valence.  ... 
doi:10.1504/ijaip.2017.081178 fatcat:dl2p75whibca3aapkeywh3goui

Multiway clustering via tensor block models [article]

Miaoyan Wang, Yuchen Zeng
2021 arXiv   pre-print
A sparse regularization is further developed for identifying important blocks with elevated means.  ...  We propose a tensor block model, develop a unified least-square estimation, and obtain the theoretical accuracy guarantees for multiway clustering.  ...  ,r K ∈ R R 1 ×···×R K is a core tensor consisting of block means, M k ∈ {0, 1} d k ×R k is a membership matrix indicating the block allocations along mode k for k ∈ [K], and E = ε i 1 ,...  ... 
arXiv:1906.03807v4 fatcat:r4s77urkq5f6hcmpcfefepd73u

Table of Contents

2020 IEEE Transactions on Signal Processing  
Li 1500 Nonlinear Adaptive Filtering With Kernel Set-Membership Approach . . . . . K. Chen, S. Werner, A. Kuh, and Y.-F.  ...  Gao, and K. Huang 2170 Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks . . . . . J. Zhang, K. You, and K.  ... 
doi:10.1109/tsp.2020.3042287 fatcat:nh7viihaozhd7li3txtadnx5ui

Sparse Subspace Clustering via Group Sparse Coding [chapter]

Budhaditya Saha, Duc Son Pham, Dinh Phung, Svetha Venkatesh
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
Sparse subspace representation is an emerging and powerful approach for clustering of data, whose generative model is a union of subspaces.  ...  We evaluate the proposed methods over a wide range of large-scale clustering problems: from challenging health care to image and text clustering benchmarks datasets and show that they outperform state-of-the-art  ...  Construct a cluster membership matrix V with element v ij = I ID K (i)=ID K (j) .  ... 
doi:10.1137/1.9781611972832.15 dblp:conf/sdm/PhamPSV13 fatcat:vkom6rjb2bfb5eso7rr7k4cn4q

Common field-of-view of cameras in robotic swarms

Chen Zhu, Christoph Bamann, Patrick Henkel, Christoph Gunther
2013 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Cooperative swarms of robots with cameras can provide stereo and multi-view vision. They are robust against failures and introduce diversity in the case of poor conditions.  ...  We propose an adaptive common FOV detection method based on fuzzy plane clustering. The performance of the method is shown to be invariant under baseline scaling.  ...  This allocates every point to a unique cluster. Alternatively, fuzzy clustering defines a level of membership u ik ∈ [0, 1] of point i in cluster k [10] .  ... 
doi:10.1109/iros.2013.6697162 dblp:conf/iros/ZhuBH013 fatcat:gqsd3hjs6nflfoip3utpka275m

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping

Marie Courbariaux, Kylliann De Santiago, Cyril Dalmasso, Fabrice Danjou, Samir Bekadar, Jean-Christophe Corvol, Maria Martinez, Marie Szafranski, Christophe Ambroise
2022 Frontiers in Genetics  
genetic analysis and 2) the concomitant clustering of clinical and genetic variables.  ...  Most methods dealing with multiple sources of information rely on data transformation, and in disease subtyping, the two main strategies used are 1) the clustering of clinical data followed by posterior  ...  Sun et al. (2014) propose a multi-view co-clustering method based on Sparse Singular Value Decomposition (Lee et al., 2010) .  ... 
doi:10.3389/fgene.2022.859462 pmid:35734430 pmcid:PMC9207464 fatcat:er3oe3l3xbhirlldsesjzesnoa

Time-shifted Pilot-based Scheduling with Adaptive Optimization for Pilot Contamination Reduction in Massive MIMO

Ambala Pradeep Kumar, Tadisetty Srinivasulu
2020 Journal of Telecommunications and Information Technology  
Here, user grouping is performed with the sparse fuzzy c-means (Sparse FCM) algorithm, grouping users based on such parameters as large-scale fading factor, SINR, and user distance.  ...  The efficiency of the adaptive ESMO approach is evaluated and reveals superior performance with the highest achievable uplink rate of 43.084 bps/Hz, the highest SINR of 132.9 dB, and maximum throughput  ...  Set cluster centers and attribute weights as q and ω.  ... 
doi:10.26636/jtit.2020.145020 fatcat:zzmvdwjcvfg5llyqhmgvzrjdcu

Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation

Elena Quatrini, Silvia Colabianchi, Francesco Costantino, Massimo Tronci
2022 Applied Sciences  
With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying  ...  Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.  ...  Another contribution, again with the help of k-means, suggests the application of K-SVD-based sparse representation method clustering to implement dictionary learning and extract information from vibration  ... 
doi:10.3390/app12020814 fatcat:cy76htoizzhy3ash432a74m6ky

TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification [article]

Wei Zhang, Zhaohong Deng, Qiongdan Lou, Te Zhang, Kup-Sze Choi, Shitong Wang
2021 arXiv   pre-print
The proposed method has the following distinctive characteristics: 1) it can deal with the incomplete and few labeled multi-view data simultaneously; 2) it integrates the missing view imputation and model  ...  Traditional multi-view learning methods rely on a large number of labeled and completed multi-view data.  ...  Third, to obtain the optimal view weight allocation, the adaptive view weighting mechanism is introduced into the model, which also further improves the adaptability of the model.  ... 
arXiv:2110.05610v3 fatcat:6idaq5kuanbfrpfxc3uzuysx5a

Evaluation of Scale Effect of Fragmented Agricultural Land Transfer Based on Neural Network

Lingjuan Tai, Linhong Li, Jun Du
2018 NeuroQuantology  
Then, the adaptive fuzzy neural network model can be trained and tested and the convergence results of the model are good.  ...  In order to improve the accuracy of the evaluation, the combination of qualitative and quantitative methods is used to screen the indicators, and the subtraction clustering method is used to obtain the  ...  Each node i in this layer is an adaptive node with a node function.  ... 
doi:10.14704/nq.2018.16.5.1394 fatcat:bwdpsoid2jhrbnsywxbjc26j2m
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