A Convex Approach to K-Means Clustering and Image Segmentation
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[1] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[2] Jiming Peng,et al. Advanced Optimization Laboratory Title : Approximating K-means-type clustering via semidefinite programming , 2005 .
[3] J. Reese,et al. Solution methods for the p-median problem: An annotated bibliography , 2006 .
[4] Jiming Peng,et al. A new theoretical framework for K-means-type clustering , 2004 .
[5] Antonin Chambolle,et al. A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.
[6] Guojun Gan,et al. Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability) , 2007 .
[7] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[8] J. Suykens,et al. Convex Clustering Shrinkage , 2005 .
[9] Shuicheng Yan,et al. Convex Optimization Procedure for Clustering: Theoretical Revisit , 2014, NIPS.
[10] Shi Li,et al. Approximating k-median via pseudo-approximation , 2012, STOC '13.
[11] M. J. van der Laan,et al. A new partitioning around medoids algorithm , 2003 .
[12] Laurent Condat,et al. A Fast Projection onto the Simplex and the l 1 Ball , 2015 .
[13] Xiaolin Wu,et al. Optimal Quantization by Matrix Searching , 1991, J. Algorithms.
[14] Xue-Cheng Tai,et al. A Continuous Max-Flow Approach to Minimal Partitions with Label Cost Prior , 2011, SSVM.
[15] Xue-Cheng Tai,et al. A Continuous Max-Flow Approach to Potts Model , 2010, ECCV.
[16] M. Emre Celebi,et al. Improving the performance of k-means for color quantization , 2011, Image Vis. Comput..
[17] D. Mumford,et al. Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .
[18] Daniel Cremers,et al. Global Solutions of Variational Models with Convex Regularization , 2010, SIAM J. Imaging Sci..
[19] L. Ljung,et al. Clustering using sum-of-norms regularization: With application to particle filter output computation , 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).
[20] Nelly Pustelnik,et al. Proximity Operator of a Sum of Functions; Application to Depth Map Estimation , 2017, IEEE Signal Processing Letters.
[21] Ravishankar Krishnaswamy,et al. Relax, No Need to Round: Integrality of Clustering Formulations , 2014, ITCS.
[22] Jianhong Wu,et al. Data clustering - theory, algorithms, and applications , 2007 .
[23] Marc Pollefeys,et al. What is optimized in convex relaxations for multilabel problems: connecting discrete and continuously inspired MAP inference. , 2014, IEEE transactions on pattern analysis and machine intelligence.
[24] Pierre Hansen,et al. NP-hardness of Euclidean sum-of-squares clustering , 2008, Machine Learning.
[25] Heinz H. Bauschke,et al. Convex Analysis and Monotone Operator Theory in Hilbert Spaces , 2011, CMS Books in Mathematics.
[26] Xavier Bresson,et al. Completely Convex Formulation of the Chan-Vese Image Segmentation Model , 2012, International Journal of Computer Vision.
[27] Trevor Darrell,et al. An efficient projection for l1, ∞ regularization , 2009, ICML '09.
[28] Laurent Condat,et al. Discrete Total Variation: New Definition and Minimization , 2017, SIAM J. Imaging Sci..
[29] Eric C. Chi,et al. Splitting Methods for Convex Clustering , 2013, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.
[30] Laurent Condat,et al. A Primal–Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms , 2012, Journal of Optimization Theory and Applications.
[31] Daniel Cremers,et al. A Convex Approach to Minimal Partitions , 2012, SIAM J. Imaging Sci..
[32] N. Sloane,et al. The Optimal Lattice Quantizer in Three Dimensions , 1983 .
[33] Meena Mahajan,et al. The Planar k-means Problem is NP-hard I , 2009 .
[34] Tony F. Chan,et al. Mumford and Shah Model and Its Applications to Image Segmentation and Image Restoration , 2015, Handbook of Mathematical Methods in Imaging.
[35] Rachid Deriche,et al. A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.
[36] Biing-Hwang Juang,et al. Optimal quantization of LSP parameters , 1993, IEEE Trans. Speech Audio Process..
[37] Francis R. Bach,et al. Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties , 2011, ICML.
[38] Xuecheng Tai,et al. Simultaneous Convex Optimization of Regions and Region Parameters in Image Segmentation Models , 2013, Innovations for Shape Analysis, Models and Algorithms.
[39] Xue-Cheng Tai,et al. Efficient Global Minimization Methods for Image Segmentation Models with Four Regions , 2014, Journal of Mathematical Imaging and Vision.
[40] Douglas Steinley,et al. K-means clustering: a half-century synthesis. , 2006, The British journal of mathematical and statistical psychology.