Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A note on the orthonormal discriminant vector method for feature extraction
Pattern Recognition
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
GTM: the generative topographic mapping
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
ACM Computing Surveys (CSUR)
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
DALASS: Variable selection in discriminant analysis via the LASSO
Computational Statistics & Data Analysis
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
Parsimonious Gaussian mixture models
Statistics and Computing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm
Journal of Multivariate Analysis
Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA
Advances in Data Analysis and Classification
Dimension reduction for model-based clustering via mixtures of multivariate $$t$$t-distributions
Advances in Data Analysis and Classification
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
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Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent orthonormal discriminative subspace with an intrinsic dimension lower than the dimension of the original space. By constraining model parameters within and between groups, a family of 12 parsimonious DLM models is exhibited which allows to fit onto various situations. An estimation algorithm, called the Fisher-EM algorithm, is also proposed for estimating both the mixture parameters and the discriminative subspace. Experiments on simulated and real datasets highlight the good performance of the proposed approach as compared to existing clustering methods while providing a useful representation of the clustered data. The method is as well applied to the clustering of mass spectrometry data.