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Image clustering is one of the most important computer vision applications, which has been extensively studied in literature.
The hungarian method for the assignment problem
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Least squares quantization in pcm
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Fcm: The fuzzy c-means clustering algorithm
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Gradient-based learning applied to document recognition
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Statistical learning theory
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A mcmc approach to hierarchical mixture modelling
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Assessing a mixture model for clustering with the integrated completed likelihood
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Normalized cuts and image segmentation
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An online algorithm for segmenting time series
E. Keogh, S. Chu, D. Hart, and M. Pazzani · 2001
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On spectral clustering: Analysis and an algorithm
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The cmu pose, illumination, and expression (pie) database
T. Sim, S. Baker, and M. Bsat · 2002
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Classification with hybrid generative/discriminative models
R. Raina, Y. Shen, A. Y. Ng, and A. McCallum · 2003
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Feature selection in clustering problems
V. Roth and T. Lange · 2003
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Document clustering based on non-negative matrix factorization
W. Xu, X. Liu, and Y. Gong · 2003
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Density-connected subspace clustering for high-dimensional data
K. Kailing, H.-P. Kriegel, and P. Kröger · 2004
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Minimum entropy clustering and applications to gene expression analysis
H. Li, K. Zhang, and T. Jiang · 2004
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Maximum margin clustering
L. Xu, J. Neufeld, B. Larson, and D. Schuurmans · 2004
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Self-tuning spectral clustering
L. Zelnik-Manor and P. Perona · 2004
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Kernelized infomax clustering
D. Barber and F. V. Agakov · 2005
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Bayesian hierarchical clustering
K. A. Heller and Z. Ghahramani · 2005
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Analysis of head gesture and prosody patterns for prosody-driven head-gesture animation
M. E. Sargin, Y. Yemez, E. Erzin, and A. M. Tekalp · 2008
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Spectral clustering for a large data set by reducing the similarity matrix size
H. Shinnou and M. Sasaki · 2008
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Discriminative k-means for clustering
J. Ye, Z. Zhao, and M. Wu · 2008
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Efficient multiclass maximum margin clustering
B. Zhao, F. Wang, and C. Zhang · 2008
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Discriminative clustering by regularized information maximization
A. Krause, P. Perona, and R. G. Gomes · 2010
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Consensus spectral clustering
D. Luo, C. Ding, and H. Huang · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol · 2010
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Semi-supervised learning using an unsupervised atlas
N. Pitelis, C. Russell, and L. Agapito · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Learning deep representations for graph clustering
F. Tian, B. Gao, Q. Cui, E. Chen, and T.-Y. Liu · 2014
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A deep semi-nmf model for learning hidden representations
G. Trigeorgis, K. Bousmalis, S. Zafeiriou, and B. Schuller · 2014
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Unsupervised feature selection via unified trace ratio formulation and k-means clustering (track)
D. Wang, F. Nie, and H. Huang · 2014
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Multi-view subspace clustering
H. Gao, F. Nie, X. Li, and H. Huang · 2015
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Image clustering using local discriminant models and global integration
Y. Yang, D. Xu, F. Nie, S. Yan, and Y. Zhuang · 2010
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Large scale spectral clustering with landmark-based representation
X. Chen and D. Cai · 2011
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Spectral embedded clustering: A framework for in-sample and out-of-sample spectral clustering
F. Nie, Z. Zeng, I. W. Tsang, D. Xu, and C. Zhang · 2011
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The manifold tangent classifier
S. Rifai, Y. N. Dauphin, P. Vincent, Y. Bengio, and X. Muller · 2011
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Contractive auto-encoders: Explicit invariance during feature extraction
S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio · 2011
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Face recognition in unconstrained videos with matched background similarity
L. Wolf, T. Hassner, and I. Maoz · 2011
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Large-scale multi-view spectral clustering via bipartite graph
Y. Li, F. Nie, H. Huang, and J. Huang · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
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Retrieving target gestures toward speech driven animation with meaningful behaviors
N. Sadoughi and C. Busso · 2015
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Msp-avatar corpus: Motion capture recordings to study the role of discourse functions in the design of intelligent virtual agents
N. Sadoughi, Y. Liu, and C. Busso · 2015
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A joint optimization framework of sparse coding and discriminative clustering
Z. Wang, Y. Yang, S. Chang, J. Li, S. Fong, and T. S. Huang · 2015
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Integrating image clustering and codebook learning
P. Xie and E. P. Xing · 2015
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Stacked what-where auto-encoders
J. Zhao, M. Mathieu, R. Goroshin, and Y. Lecun · 2015
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New robust clustering model for identifying cancer genome landscapes
H. Gao, X. Wang, and H. Huang · 2016
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New l1-norm relaxations and optimizations for graph clustering
F. Nie, C. Deng, H. Wang, X. Gao, and H. Huang · 2016
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Subspace clustering via new discrete group structure constrained low-rank model
F. Nie and H. Huang · 2016
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The constrained laplacian rank algorithm for graph-based clustering
F. Nie, X. Wang, M. I. Jordan, and H. Huang · 2016
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Structured doubly stochastic matrix for graph based clustering: Structured doubly stochastic matrix
X. Wang, F. Nie, and H. Huang · 2016
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Learning a task-specific deep architecture for clustering
Z. Wang, S. Chang, J. Zhou, M. Wang, and T. S. Huang · 2016
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Unsupervised deep embedding for clustering analysis
J. Xie, R. Girshick, and A. Farhadi · 2016
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Joint unsupervised learning of deep representations and image clusters
J. Yang, D. Parikh, and D. Batra · 2016
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Semantic image inpainting with perceptual and contextual losses
R. Yeh, C. Chen, T. Y. Lim, M. Hasegawa-Johnson, and M. N. Do · 2016
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