High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length
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[1] Jorma Rissanen,et al. The Minimum Description Length Principle in Coding and Modeling , 1998, IEEE Trans. Inf. Theory.
[2] David L. Dowe,et al. MML Clustering of Continuous-Valued Data Using Gaussian and t Distributions , 2002, Australian Joint Conference on Artificial Intelligence.
[3] Ravi Kothari,et al. On finding the number of clusters , 1999, Pattern Recognit. Lett..
[4] Jitendra Malik,et al. Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[5] C. S. Wallace,et al. An Information Measure for Classification , 1968, Comput. J..
[6] H. Bozdogan. Determining the Number of Component Clusters in the Standard Multivariate Normal Mixture Model Using Model-Selection Criteria. , 1983 .
[7] Rohan A. Baxter,et al. MML and Bayesianism: similarities and differences: introduction to minimum encoding inference Part , 1994 .
[8] Michael Unser,et al. Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Gerard Salton,et al. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer , 1989 .
[10] David L. Dowe,et al. Unsupervised Learning of Correlated Multivariate Gaussian Mixture Models Using MML , 2003, Australian Conference on Artificial Intelligence.
[11] P. Deb. Finite Mixture Models , 2008 .
[12] Anil K. Jain,et al. Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[13] C. S. Wallace,et al. Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics) , 2005 .
[14] C. S. Wallace,et al. Classification by Minimum-Message-Length Inference , 1991, ICCI.
[15] C. S. Wallace,et al. Estimation and Inference by Compact Coding , 1987 .
[16] R. Beckman,et al. Maximum likelihood estimation for the beta distribution , 1978 .
[17] Jing Huang,et al. Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[18] Anil K. Jain,et al. Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[19] Nizar Bouguila,et al. Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application , 2004, IEEE Transactions on Image Processing.
[20] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[21] Christos Faloutsos,et al. QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.
[22] Padhraic Smyth,et al. Model selection for probabilistic clustering using cross-validated likelihood , 2000, Stat. Comput..
[23] G. McLachlan,et al. The EM algorithm and extensions , 1996 .
[24] William D. Penny,et al. Bayesian Approaches to Gaussian Mixture Modeling , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[25] Trygve Randen,et al. Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[26] Anil K. Jain,et al. Image classification for content-based indexing , 2001, IEEE Trans. Image Process..
[27] G. McLachlan. On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture , 1987 .
[28] José M. N. Leitão,et al. On Fitting Mixture Models , 1999, EMMCVPR.
[29] Tzu-Tsung Wong,et al. Generalized Dirichlet distribution in Bayesian analysis , 1998, Appl. Math. Comput..
[30] Djemel Ziou,et al. Combining positive and negative examples in relevance feedback for content-based image retrieval , 2003, J. Vis. Commun. Image Represent..
[31] D. W. Scott. Probability Density Estimation , 2001 .
[32] Adrian E. Raftery,et al. How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..
[33] C. S. Wallace,et al. Intrinsic Classification of Spatially Correlated Data , 1998, Comput. J..
[34] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[35] H. Akaike. A new look at the statistical model identification , 1974 .
[36] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[37] Alex Pentland,et al. Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.
[38] David L. Dowe,et al. MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions , 2000, Stat. Comput..
[39] Robert J. Connor,et al. Concepts of Independence for Proportions with a Generalization of the Dirichlet Distribution , 1969 .
[40] Yudi Agusta,et al. Unsupervised learning of Gamma mixture models using Minimum Message Length , 2003 .
[41] David Haussler,et al. Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology , 1996, Comput. Appl. Biosci..
[42] Nizar Bouguila,et al. MML-Based Approach for High-Dimensional Unsupervised Learning Using the Generalized Dirichlet Mixture , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.
[43] Jorma Rissanen,et al. Universal coding, information, prediction, and estimation , 1984, IEEE Trans. Inf. Theory.
[44] Jonathan J. Oliver,et al. Finding overlapping components with MML , 2000, Stat. Comput..
[45] David L. Dowe,et al. Minimum Message Length and Kolmogorov Complexity , 1999, Comput. J..
[46] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[47] D. Ziou,et al. A powerful finite mixture model based on the generalized Dirichlet distribution: unsupervised learning and applications , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
[48] David L. Dowe,et al. Intrinsic classification by MML - the Snob program , 1994 .
[49] G. Celeux,et al. A stochastic approximation type EM algorithm for the mixture problem , 1992 .
[50] P. Green,et al. On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .
[51] A. Cohen,et al. Finite Mixture Distributions , 1982 .
[52] Calyampudi Radhakrishna Rao,et al. Advanced Statistical Methods in Biometric Research. , 1953 .
[53] Peter Grünwald,et al. Invited review of the book Statistical and Inductive Inference by Minimum Message Length , 2006 .
[54] Nizar Bouguila,et al. Unsupervised learning of a finite gamma mixture using MML: application to SAR image analysis , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
[55] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[56] Shih-Fu Chang,et al. VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.
[57] L. Wasserman,et al. Practical Bayesian Density Estimation Using Mixtures of Normals , 1997 .
[58] C. S. Wallace,et al. MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions , 1997 .
[59] N. J. A. Sloane,et al. Sphere Packings, Lattices and Groups , 1987, Grundlehren der mathematischen Wissenschaften.
[60] B. S. Manjunath,et al. Category-based image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).
[61] David L. Dowe,et al. Single Factor Analysis in MML Mixture Modelling , 1998, PAKDD.
[62] Adrian E. Raftery,et al. Inference in model-based cluster analysis , 1997, Stat. Comput..
[63] G. Reaven,et al. An attempt to define the nature of chemical diabetes using a multidimensional analysis , 2004, Diabetologia.
[64] G. Schwarz. Estimating the Dimension of a Model , 1978 .