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Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.
The hungarian method for the assignment problem
Kuhn, Harold W · 1955
Earlier work this paper cites.
Adaptive Control Processes: A Guided Tour
Bellman, R · 1961
Earlier work this paper cites.
Some methods for classification and analysis of multivariate observations
MacQueen, James et al · 1967
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Approximation capabilities of multilayer feedforward networks
Hornik, Kurt · 1991
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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Analyzing the effectiveness and applicability of co-training
Nigam, Kamal and Ghani, Rayid · 2000
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On clustering validation techniques
Halkidi, Maria, Batistakis, Yannis, and Vazirgiannis, Michalis · 2001
Earlier work this paper cites.
Distance metric learning with application to clustering with side-information
Xing, Eric P, Jordan, Michael I, Russell, Stuart, and Ng, Andrew Y · 2002
Earlier work this paper cites.
Rcv1: A new benchmark collection for text categorization research
Lewis, David D, Yang, Yiming, Rose, Tony G, and Li, Fan · 2004
Earlier work this paper cites.
Entropy-based criterion in categorical clustering
Li, Tao, Ma, Sheng, and Ogihara, Mitsunori · 2004
Earlier work this paper cites.
The challenges of clustering high dimensional data
Steinbach, Michael, Ertöz, Levent, and Kumar, Vipin · 2004
Earlier work this paper cites.
Toward integrating feature selection algorithms for classification and clustering
Liu, Huan and Yu, Lei · 2005
Earlier work this paper cites.
Pattern recognition and machine learning
Bishop, Christopher M · 2006
Earlier work this paper cites.
Discriminative cluster analysis
De la Torre, Fernando and Kanade, Takeo · 2006
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Reducing the dimensionality of data with neural networks
Hinton, Geoffrey E and Salakhutdinov, Ruslan R · 2006
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A tutorial on spectral clustering
Von Luxburg, Ulrike · 2007
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Visualizing data using t-SNE
van der Maaten, Laurens and Hinton, Geoffrey · 2008
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Learning a mahalanobis distance metric for data clustering and classification
Xiang, Shiming, Nie, Feiping, and Zhang, Changshui · 2008
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Discriminative k-means for clustering
Ye, Jieping, Zhao, Zheng, and Wu, Mingrui · 2008
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Unsupervised feature selection for the k k -means clustering problem
Boutsidis, Christos, Drineas, Petros, and Mahoney, Michael W · 2009
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What makes paris look like paris?
Doersch, Carl, Singh, Saurabh, Gupta, Abhinav, Sivic, Josef, and Efros, Alexei · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Data clustering: algorithms and applications
Aggarwal, Charu C and Reddy, Chandan K · 2013
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Feature selection for clustering: A review
Alelyani, Salem, Tang, Jiliang, and Liu, Huan · 2013
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Representation learning: A review and new perspectives
Bengio, Yoshua, Courville, Aaron, and Vincent, Pascal · 2013
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Building high-level features using large scale unsupervised learning
Le, Quoc V · 2013
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Learning a parametric embedding by preserving local structure
van der Maaten, Laurens · 2009
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Fast approximate spectral clustering
Yan, Donghui, Huang, Ling, and Jordan, Michael I · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, Vinod and Hinton, Geoffrey E · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, Pascal, Larochelle, Hugo, Lajoie, Isabelle, Bengio, Yoshua, and Manzagol, Pierre-Antoine · 2010
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Image clustering using local discriminant models and global integration
Yang, Yi, Xu, Dong, Nie, Feiping, Yan, Shuicheng, and Zhuang, Yueting · 2010
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An analysis of single-layer networks in unsupervised feature learning
Coates, Adam, Ng, Andrew Y, and Lee, Honglak · 2011
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, Ross, Donahue, Jeff, Darrell, Trevor, and Malik, Jitendra · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014
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Fully convolutional networks for semantic segmentation
Long, Jonathan, Shelhamer, Evan, and Darrell, Trevor · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014
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Learning deep representations for graph clustering
Tian, Fei, Gao, Bin, Cui, Qing, Chen, Enhong, and Liu, Tie-Yan · 2014
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Accelerating t-SNE using tree-based algorithms
van Der Maaten, Laurens · 2014
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Visualizing and understanding convolutional networks
Zeiler, Matthew D and Fergus, Rob · 2014
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