High-dimensional data clustering
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[1] Stáephane Girard,et al. A nonlinear PCA based on manifold approximation , 2000, Comput. Stat..
[2] Benzion Boukai,et al. The Discrimination Subspace Model , 1997 .
[3] GunopulosDimitrios,et al. Automatic subspace clustering of high dimensional data for data mining applications , 1998 .
[4] R. Cattell. The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.
[5] W. Gautschi,et al. An algorithm for simultaneous orthogonal transformation of several positive definite symmetric matrices to nearly diagonal form , 1986 .
[6] W. V. McCarthy,et al. Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data , 1995 .
[7] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[8] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[9] J. Bezdek,et al. Detection and Characterization of Cluster Substructure II. Fuzzy c-Varieties and Convex Combinations Thereof , 1981 .
[10] James R. Schott. Dimensionality reduction in quadratic discriminant analysis , 1993 .
[11] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[12] Joseph S. Verducci,et al. Multivariate Statistical Modeling and Data Analysis. , 1988 .
[13] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[14] Adrian E. Raftery,et al. Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .
[15] M. Schader,et al. New Approaches in Classification and Data Analysis , 1994 .
[16] J. B. Ramsey,et al. Estimating Mixtures of Normal Distributions and Switching Regressions , 1978 .
[17] T. Pavlenko,et al. Effect of dimensionality on discrimination , 2001 .
[18] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[19] Sean R Eddy,et al. What is dynamic programming? , 2004, Nature Biotechnology.
[20] A. Raftery,et al. Variable Selection for Model-Based Clustering , 2006 .
[21] Teuvo Kohonen,et al. Self-Organizing Maps , 2010 .
[22] Christopher M. Bishop,et al. Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.
[23] Chao Yang,et al. ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods , 1998, Software, environments, tools.
[24] ZhangJ.,et al. Local Features and Kernels for Classification of Texture and Object Categories , 2007 .
[25] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[26] G. McLachlan,et al. The EM algorithm and extensions , 1996 .
[27] J. Carroll,et al. K-means clustering in a low-dimensional Euclidean space , 1994 .
[28] Geoffrey J. McLachlan,et al. Modelling high-dimensional data by mixtures of factor analyzers , 2003, Comput. Stat. Data Anal..
[29] A. Raftery,et al. Model-based Gaussian and non-Gaussian clustering , 1993 .
[30] I. Jolliffe. Principal Component Analysis , 2002 .
[31] Maurizio Vichi,et al. A mixture model for the classification of three-way proximity data , 2006, Comput. Stat. Data Anal..
[32] Cordelia Schmid,et al. The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.
[33] G. Celeux,et al. A Classification EM algorithm for clustering and two stochastic versions , 1992 .
[34] Michael I. Jordan,et al. Mixtures of Probabilistic Principal Component Analyzers , 2001 .
[35] J. Bezdek,et al. DETECTION AND CHARACTERIZATION OF CLUSTER SUBSTRUCTURE I. LINEAR STRUCTURE: FUZZY c-LINES* , 1981 .
[36] E. Diday,et al. Introduction à l'analyse factorielle typologique , 1974 .
[37] Hans-Hermann Bock,et al. On the Interface between Cluster Analysis, Principal Component Analysis, and Multidimensional Scaling , 1987 .
[38] C. Schmid,et al. Object Class Recognition Using Discriminative Local Features , 2005 .
[39] Gérard Govaert,et al. Gaussian parsimonious clustering models , 1995, Pattern Recognit..
[40] Huan Liu,et al. Subspace clustering for high dimensional data: a review , 2004, SKDD.
[41] T. Pavlenko. On feature selection, curse-of-dimensionality and error probability in discriminant analysis , 2003 .
[42] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[43] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[44] B. Flury. Common Principal Components in k Groups , 1984 .
[45] François Poulet,et al. OMEGA: Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité , 2004 .
[46] H. Bock. Probabilistic models in cluster analysis , 1996 .
[47] Jeanny Hérault,et al. Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets , 1997, IEEE Trans. Neural Networks.
[48] W. DeSarbo,et al. A maximum likelihood methodology for clusterwise linear regression , 1988 .
[49] T. Hastie,et al. Principal Curves , 2007 .