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Flexible and robust co-regularized multi-domain graph clustering
2013
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13
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In this paper, we propose a flexible and robust framework, CGC (Co-regularized Graph Clustering), based on non-negative matrix factorization (NMF), to tackle these challenges. ...
Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. ...
In this paper, we propose CGC (Co-regularized Graph Clustering), a flexible and robust approach to integrate heterogenous graph data. Our contributions are summarized as follows. 1. ...
doi:10.1145/2487575.2487582
dblp:conf/kdd/ChengZGWSW13
fatcat:l2a4qexocbcyll2iqnlku67neu
Flexible and Robust Multi-Network Clustering
2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
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Various multi-view and multi-domain graph clustering methods have recently been developed to integrate multiple networks. In these methods, a network is treated as a view or domain. ...
In this paper, we propose a flexible and robust framework that allows multiple underlying clustering structures across different networks. ...
(TF) [16] ; and (7) multi-domain co-regularized graph clustering (CGC) [8] . ...
doi:10.1145/2783258.2783262
dblp:conf/kdd/NiTFZ15
fatcat:fm6cczgnzbdc5lyakfdoclzc24
CGC
2016
ACM Transactions on Knowledge Discovery from Data
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In this paper, we propose a flexible and robust framework, CGC (Co-regularized Graph Clustering), based on non-negative matrix factorization (NMF), to tackle these challenges. ...
Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. ...
We apply CGC (with RSS loss) to cluster the generated multi-domain graphs with two different settings: (1) equal weights for each cross-domain regularizer; (2) optimal weights for each cross-domain relationship ...
doi:10.1145/2903147
pmid:29081726
pmcid:PMC5658064
fatcat:3efq6rpeanhv3mrsxh3trwnpoy
Introduction to the Issue on Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications
2018
IEEE Journal on Selected Topics in Signal Processing
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To remedy high computation complexity in graph-based methods, Yu et al. propose an unsupervised graph-based dimensionality reduction method named Fast and Flexible Large Graph Embedding based on anchors ...
l 2 -norm regularization with locality-constrained property into graph construction. ...
doi:10.1109/jstsp.2018.2879245
fatcat:z3ohqdl37nat3pjo65fzsf2ady
Multi-view Subspace Clustering via Partition Fusion
[article]
2019
arXiv
pre-print
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Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. ...
Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. ...
on concatenated features is used as a baseline algorithm. • Co-regularized multi-view spectral clustering (Co-reg) [25] : A co-regularization mechnism is utilized to ensure that partitions from different ...
arXiv:1912.01201v1
fatcat:v6yj6mnycbajdc5ajby35gt2qy
Learning Robust Data Representation: A Knowledge Flow Perspective
[article]
2020
arXiv
pre-print
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It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain ...
This survey covers the topic from a knowledge flow perspective in terms of: (1) robust knowledge recovery, (2) robust knowledge transfer, and (3) robust knowledge fusion, centered around several major ...
Following this, we proposed a multi-view clustering algorithm with ensemble strategy, adopting multiple co-association matrices as the input to seek a low-rank common representation [Tao et al., 2017] ...
arXiv:1909.13123v2
fatcat:wll23rkrznejvhzsihc6rwcwve
New Approaches in Multi-View Clustering
[chapter]
2018
Recent Applications in Data Clustering
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To provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, this chapter summarizes five kinds of popular clustering methods and their multi-view ...
To promote further research and development of multi-view clustering, some popular and open datasets are summarized in two categories. ...
In [49] , a flexible and robust NMF-based framework, named co-regularized graph clustering (CGC), is developed to address the multi-domain graph clustering problem. ...
doi:10.5772/intechopen.75598
fatcat:jniifuf4ync27fofz4fpbnfiia
A unified framework based on graph consensus term for multi-view learning
[article]
2021
arXiv
pre-print
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Due to more compatible and complementary information from multiple views, existing multi-view methods could achieve more promising performance than conventional single-view methods in most situations. ...
In this paper, we propose a novel multi-view learning framework, which aims to leverage most existing graph embedding works into a unified formula via introducing the graph consensus term. ...
This work was supported by National Natural Science Foundation of PR China(61672130, 61972064) and LiaoNing Revitalization Talents Program(XLYC1806006). ...
arXiv:2105.11781v2
fatcat:zdwgzl24fzdpdayh3cooea2ufy
A Comprehensive Survey on Community Detection with Deep Learning
[article]
2021
arXiv
pre-print
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We then discuss the practical applications of community detection in various domains and point to implementation scenarios. ...
The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. ...
neural networks
One2Multi
One-view to Multi-view
[69]
One2multi graph autoencoder for multi-view graph clustering
One2Multi Graph Autoencoder for Multi-view
O2MAC
Graph Clustering
[69]
One2multi ...
arXiv:2105.12584v2
fatcat:matipshxnzcdloygrcrwx2sxr4
2019 Index IEEE Transactions on Knowledge and Data Engineering Vol. 31
2020
IEEE Transactions on Knowledge and Data Engineering
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Jiang, L., +,
TKDE Feb. 2019 201-213
CFOND: Consensus Factorization for Co-Clustering Networked Data. ...
., +, TKDE Nov. 2019 2035-2050
Co-Clustering via Information-Theoretic Markov Aggregation. Blochl, C.,
+, TKDE April 2019 720-732
Efficient Mining of Frequent Patterns on Uncertain Graphs. ...
doi:10.1109/tkde.2019.2953412
fatcat:jkmpnsjcf5a3bhhf4ian66mj5y
Agglomerative Neural Networks for Multi-view Clustering
[article]
2020
arXiv
pre-print
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Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. ...
We further demonstrate ANN's capability in analyzing complex view structures and extensibility in our case study and explain its robustness and effectiveness of data-driven modifications. ...
(Co-reg) [12] , Binary Multi-view clustering (BMVC) [19] , Graph Learning for Multi-view Clustering (MVGL) [16] , Self-weighted Multi-view Clustering (SWMC) [20] , Multi-view Learning with Adaptive ...
arXiv:2005.05556v1
fatcat:bdn4gclravfjpbzad2rgyzj2fy
Unsupervised Multi-Class Co-Segmentation via Joint-Cut Over $L_{1}$ -Manifold Hyper-Graph of Discriminative Image Regions
2017
IEEE Transactions on Image Processing
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This paper systematically advocates a robust and efficient unsupervised multi-class co-segmentation approach by leveraging underlying subspace manifold propagation to exploit the cross-image coherency. ...
As for the inter-image coherency, we leverage multi-type features involved L 1 -graph to detect the underlying local manifold from cross-image regions. ...
It gives rise to a novel and flexible unsupervised multi-class co-segmentation framework. ...
doi:10.1109/tip.2016.2631883
pmid:28114015
fatcat:7j6dtqr4zfdcxmzshz7gya4w2i
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. ...
Chen, P., +, TIP 2021 3279-3292 Dimensionality reduction Flexible Multi-View Unsupervised Graph Embedding. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Multitask Linear Discriminant Analysis for View Invariant Action Recognition
2014
IEEE Transactions on Image Processing
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In particular, we propose two variants of graphguided multitask LDA: 1) where the graph weights specifying view dependencies are fixed a priori and 2) where graph weights are flexibly learnt from the training ...
Robust action recognition under viewpoint changes has received considerable attention recently. ...
, and (2) Multi-task flexible graph-guided LDA where the graph weights are flexibly learned from (or iteratively refined based on) training features. ...
doi:10.1109/tip.2014.2365699
pmid:25361507
fatcat:cdh5pwnt2fhgrdfedcpbe6cksi
Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries
[article]
2020
arXiv
pre-print
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In this paper, we review modern signal processing frameworks generalizing GSP to multi-way data, starting from graph signals coupled to familiar regular axes such as time in sensor networks, and then extending ...
We synthesize common themes arising from current efforts to combine GSP with tensor analysis and highlight future directions in extending GSP to the multi-way paradigm. ...
The multiscale decompositions are constructed either from data-adaptive tree transforms [6] or through a series of multi-way graph-based co-clustering solutions [32] .
VI. ...
arXiv:2007.00041v2
fatcat:i2e77o5njrhkpfoxtdswdkpibm
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