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Active Clustering with Model-Based Uncertainty Reduction

Caiming Xiong, David M. Johnson, Jason J. Corso
2017 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Here, we propose a novel online framework for active semi-supervised spectral clustering that selects pairwise constraints as clustering proceeds, based on the principle of uncertainty reduction.  ...  This may require a large number of constraints, some of which could be redundant, unnecessary, or even detrimental to the clustering results.  ...  Spectral clustering with pairwise constraints Spectral clustering is a well-known unsupervised clustering method [34] .  ... 
doi:10.1109/tpami.2016.2539965 pmid:26978555 fatcat:yvrb433525hipjmsy6nqx4qsdm

Active Clustering with Model-Based Uncertainty Reduction [article]

Caiming Xiong, David Johnson, Jason J. Corso
2014 arXiv   pre-print
Here, we propose a novel online framework for active semi-supervised spectral clustering that selects pairwise constraints as clustering proceeds, based on the principle of uncertainty reduction.  ...  This may require a large number of constraints, some of which could be redundant, unnecessary, or even detrimental to the clustering results.  ...  Spectral clustering with pairwise constraints Spectral clustering is a well-known unsupervised clustering method [34] .  ... 
arXiv:1402.1783v2 fatcat:4z7mypvwyjgl7lba6pm52qy4na

Constrained clustering via spectral regularization

Zhenguo Li, Jianzhuang Liu, Xiaoou Tang
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
We propose a novel framework for constrained spectral clustering with pairwise constraints which specify whether two objects belong to the same cluster or not.  ...  Our formulation leads to a small semidefinite program whose complexity is independent of the number of objects in the data set and the number of pairwise constraints, making it scalable to large-scale  ...  constraints, making it scale well to large-scale problems.  ... 
doi:10.1109/cvprw.2009.5206852 fatcat:ho6v5kxgzjh6lgrwmzy5fe4jam

Constrained clustering via spectral regularization

Zhenguo Li, Jianzhuang Liu, Xiaoou Tang
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
We propose a novel framework for constrained spectral clustering with pairwise constraints which specify whether two objects belong to the same cluster or not.  ...  Our formulation leads to a small semidefinite program whose complexity is independent of the number of objects in the data set and the number of pairwise constraints, making it scalable to large-scale  ...  constraints, making it scale well to large-scale problems.  ... 
doi:10.1109/cvpr.2009.5206852 dblp:conf/cvpr/LiLT09 fatcat:hrqawncby5e6rp5bv432lt5wgm

Deep learning vs spectral clustering into an active clustering with pairwise constraints propagation

Nicolas Voiron, Alexandre Benoit, Patrick Lambert, Bogdan Ionescu
2016 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)  
In such use case, Spectral Clustering proved to be an efficient method.  ...  In this paper, we firstly introduce the concept of Deep Learning into an active semi-supervised clustering process and compare it with Spectral Clustering.  ...  Such solution naturally competes with the Spectral Clustering process in order to address large scale clustering scenarios.  ... 
doi:10.1109/cbmi.2016.7500237 dblp:conf/cbmi/VoironBLI16 fatcat:mlw4g2ilyve7dnkj42dvpblapm

Constrained clustering by spectral kernel learning

Zhenguo Li, Jianzhuang Liu
2009 2009 IEEE 12th International Conference on Computer Vision  
; 4) it is scalable to large-scale problems; and 5) it can handle weighted pairwise constraints.  ...  In this paper, we consider constrained clustering with pairwise constraints, which specify some pairs of objects from the same cluster or not.  ...  pairwise constraints effectively; 4) it is scalable to large-scale problems with large numbers of clusters; and 5) it can handle weighted pairwise constraints.  ... 
doi:10.1109/iccv.2009.5459157 dblp:conf/iccv/LiL09 fatcat:qbqhssycdjflfcnrnj6mzf55qu

Pairwise Constraint Propagation: A Survey [article]

Zhenyong Fu, Zhiwu Lu
2015 arXiv   pre-print
As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has  ...  This paper provides an up-to-date critical survey of pairwise constraint propagation research.  ...  Although the E 2 CP constraint propagation and similarity adjustment approaches are proposed in the context of constrained spectral clustering based on pairwise constraint propagation, they can be readily  ... 
arXiv:1502.05752v1 fatcat:djagaxttkjawpjfzys2q476zom

Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge

Gui-Song Xia, Zifeng Wang, Caiming Xiong, Liangpei Zhang
2015 Remote Sensing  
graph-based spectral clustering algorithm and pairwise constraints that are incrementally added via active learning.  ...  The pairwise constraints are simply similarity/dissimilarity relationships between the most uncertain pairwise nodes on the graph, which can be easily determined by non-expert human oracles.  ...  To address large-scale remote sensing image data, certain large-scale spectral clustering algorithms will take less time to perform the clustering.  ... 
doi:10.3390/rs71115014 fatcat:jwbzycqrgnhr3jwiiqrlp43zae

BoostCluster

Yi Liu, Rong Jin, Anil K. Jain
2007 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07  
Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints.  ...  However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints.  ...  There are two major approaches to semi-supervised clustering: the constraint-based approach and the approach based on distance metric learning.  ... 
doi:10.1145/1281192.1281242 dblp:conf/kdd/LiuJJ07 fatcat:4kixzayh7rd4ndk24pgykdu7cy

Large-Scale Spectral Clustering Based on Representative Points

Libo Yang, Xuemei Liu, Feiping Nie, Mingtang Liu
2019 Mathematical Problems in Engineering  
In order to achieve fast spectral clustering, we propose a novel approach, called representative point-based spectral clustering (RPSC), to efficiently deal with the large-scale spectral clustering problem  ...  However, most traditional spectral clustering methods still face challenges in the successful application of large-scale spectral clustering problems mainly due to their high computational complexity οn3  ...  Semertzidis et al. proposed an efficient spectral clustering method for large-scale data sets in which a set of pairwise constraints were given to increase clustering accuracy and reduce clustering complexity  ... 
doi:10.1155/2019/5864020 fatcat:zvd737rgo5b3jidxpnoqbts3i4

Semi-supervised spectral clustering with automatic propagation of pairwise constraints

Nicolas Voiron, Alexandre Benoit, Andrei Filip, Patrick Lambert, Bogdan Ionescu
2015 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)  
In this context, this paper studies the impact of pairwise constraints to unsupervised Spectral Clustering.  ...  On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data.  ...  This method can deal with large datasets when working on sparse similarity graphs, so it is best candidate for approaching large scale video clustering.  ... 
doi:10.1109/cbmi.2015.7153608 dblp:conf/cbmi/VoironBFLI15 fatcat:kknvn3wpbzf7fdzx3ajdmdgiui

Constrained Multi-View Video Face Clustering

Xiaochun Cao, Changqing Zhang, Chengju Zhou, Huazhu Fu, Hassan Foroosh
2015 IEEE Transactions on Image Processing  
In this paper, we focus on face clustering in videos.  ...  graph-based model.  ...  Finally, based on these constrained sparse representations, we apply multi-view spectral clustering with pairwise constraints on the similarity matrix to get the final clustering result. A.  ... 
doi:10.1109/tip.2015.2463223 pmid:26259245 fatcat:sopxi5lcwzh7zomsuo4eixhegm

Exhaustive and Efficient Constraint Propagation: A Graph-Based Learning Approach and Its Applications

Zhiwu Lu, Yuxin Peng
2012 International Journal of Computer Vision  
The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering.  ...  be solved in quadratic time using label propagation based on k-nearest neighbor graphs.  ...  In this paper, for the convenience of clarifying our motivation, we focus on constrained spectral clustering, i.e., the exploitation of pairwise constraints for spectral clustering [8] - [11] which  ... 
doi:10.1007/s11263-012-0602-z fatcat:aly6m6seo5gx7ebqujzpj6xbca

Face Clustering: Representation and Pairwise Constraints

Yichun Shi, Charles Otto, Anil K. Jain
2018 IEEE Transactions on Information Forensics and Security  
large scale face retrieval.  ...  Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarity between face images.  ...  Research has also been conducted on incorporating pairwise constraints into hierarchical clustering [33] and spectral clustering [34] [35] .  ... 
doi:10.1109/tifs.2018.2796999 fatcat:e4e4ipvrvjarzi35zcakldexrq

On defining affinity graph for spectral clustering through ranking on manifolds

Tian Xia, Juan Cao, Yong-dong Zhang, Jin-tao Li
2009 Neurocomputing  
However, Gaussian function is hard to depict the intrinsic structure of the data, and it has to specify a scaling parameter whose selection is still an open issue in spectral clustering.  ...  framework of ranking on manifolds.  ...  Semi-supervised spectral clustering Compared to unsupervised clustering, some supervision in the form of pairwise constraints is provided in semi-supervised clustering.  ... 
doi:10.1016/j.neucom.2009.03.012 fatcat:3xp3kleogzhsho4pnrvwqjxmyu
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