High-dimensional data clustering

[1]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[2]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[3]  Maurizio Vichi,et al.  A mixture model for the classification of three-way proximity data , 2006, Comput. Stat. Data Anal..

[4]  A. Raftery,et al.  Variable Selection for Model-Based Clustering , 2006 .

[5]  Christopher K. I. Williams,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[6]  C. Schmid,et al.  Object Class Recognition Using Discriminative Local Features , 2005 .

[7]  François Poulet,et al.  OMEGA: Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité , 2004 .

[8]  Huan Liu,et al.  Subspace clustering for high dimensional data: a review , 2004, SKDD.

[9]  T. Pavlenko On feature selection, curse-of-dimensionality and error probability in discriminant analysis , 2003 .

[10]  Geoffrey J. McLachlan,et al.  Modelling high-dimensional data by mixtures of factor analyzers , 2003, Comput. Stat. Data Anal..

[11]  I. Jolliffe Principal Component Analysis , 2002 .

[12]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[13]  T. Pavlenko,et al.  Effect of dimensionality on discrimination , 2001 .

[14]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[15]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[16]  G. McLachlan,et al.  Finite Mixture Models , 2000, Wiley Series in Probability and Statistics.

[17]  Stáephane Girard,et al.  A nonlinear PCA based on manifold approximation , 2000, Comput. Stat..

[18]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[19]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[20]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[21]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[22]  Benzion Boukai,et al.  The Discrimination Subspace Model , 1997 .

[23]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[24]  H. Bock Probabilistic models in cluster analysis , 1996 .

[25]  Gérard Govaert,et al.  Gaussian parsimonious clustering models , 1995, Pattern Recognit..

[26]  T. Kohonen Self-Organizing Maps , 1995, Springer Series in Information Sciences.

[27]  W. V. McCarthy,et al.  Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data , 1995 .

[28]  A. Raftery,et al.  Model-based Gaussian and non-Gaussian clustering , 1993 .

[29]  James R. Schott Dimensionality reduction in quadratic discriminant analysis , 1993 .

[30]  G. Celeux,et al.  A Classification EM algorithm for clustering and two stochastic versions , 1992 .

[31]  W. DeSarbo,et al.  A maximum likelihood methodology for clusterwise linear regression , 1988 .

[32]  Arjun K. Gupta,et al.  Multivariate Statistical Modeling and Data Analysis. , 1988 .

[33]  B. Flury Common Principal Components in k Groups , 1984 .

[34]  J. Bezdek,et al.  Detection and Characterization of Cluster Substructure II. Fuzzy c-Varieties and Convex Combinations Thereof , 1981 .

[35]  J. Bezdek,et al.  DETECTION AND CHARACTERIZATION OF CLUSTER SUBSTRUCTURE I. LINEAR STRUCTURE: FUZZY c-LINES* , 1981 .

[36]  J. B. Ramsey,et al.  Estimating Mixtures of Normal Distributions and Switching Regressions , 1978 .

[37]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[38]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[39]  R. Cattell The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[40]  S. Lazebnik,et al.  Local Features and Kernels for Classification of Texture and Object Categories: An In-Depth Study , 2005 .

[41]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[42]  Chao Yang,et al.  ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods , 1998, Software, environments, tools.

[43]  Jeanny Hérault,et al.  Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets , 1997, IEEE Trans. Neural Networks.

[44]  J. Carroll,et al.  K-means clustering in a low-dimensional Euclidean space , 1994 .

[45]  M. Schader,et al.  New Approaches in Classification and Data Analysis , 1994 .

[46]  Hans-Hermann Bock,et al.  On the Interface between Cluster Analysis, Principal Component Analysis, and Multidimensional Scaling , 1987 .

[47]  W. Gautschi,et al.  An algorithm for simultaneous orthogonal transformation of several positive definite symmetric matrices to nearly diagonal form , 1986 .

[48]  E. Diday,et al.  Introduction à l'analyse factorielle typologique , 1974 .