A finite mixture model for simultaneous high-dimensional clustering, localized feature selection and outlier rejection

Authors:
Nizar Bouguila;Khaled Almakadmeh;Sabri Boutemedjet
Affiliations:
Concordia Institute for Information Systems Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, Quebec, Canada H3G 2W1;Concordia Institute for Information Systems Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, Quebec, Canada H3G 2W1;Departement Informatique, Universite de Sherbrooke, Sherbrooke, Quebec, Canada
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 31
Cited 1

On Image Analysis by the Methods of Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Divergence Based Feature Selection for Multimodal Class Densities

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Review and Empirical Evaluation of Feature Weighting Methods for aClass of Lazy Learning Algorithms

Artificial Intelligence Review - Special issue on lazy learning
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Bayesian Approaches to Gaussian Mixture Modeling

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data clustering: a review

ACM Computing Surveys (CSUR)
On Advances in Statistical Modeling of Natural Images

Journal of Mathematical Imaging and Vision
MINPRAN: A New Robust Estimator for Computer Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Mixture Model and EM-Based Algorithm for Class Discovery, Robust Classification, and Outlier Rejection in Mixed Labeled/Unlabeled Data Sets

IEEE Transactions on Pattern Analysis and Machine Intelligence
Greedy learning of multiple objects in images using robust statistics and factorial learning

Neural Computation
Simultaneous Feature Selection and Clustering Using Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Robust PCA and classification in biosciences

Bioinformatics
Feature Selection with a Linear Dependence Measure

IEEE Transactions on Computers
High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length

IEEE Transactions on Pattern Analysis and Machine Intelligence
Localized feature selection for clustering

Pattern Recognition Letters
Finite Gamma mixture modelling using minimum message length inference: application to SAR image analysis

International Journal of Remote Sensing
Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model-Based Approach for Discrete Data Clustering and Feature Weighting Using MAP and Stochastic Complexity

IEEE Transactions on Knowledge and Data Engineering
Selecting discrete and continuous features based on neighborhood decision error minimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic method for efficient computation of full and subsets Zernike moments

Information Sciences: an International Journal
A visual shape descriptor using sectors and shape context of contour lines

Information Sciences: an International Journal
Robust subspace clustering by combined use of kNND metric and SVD algorithm

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image Normalization by Complex Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Problem of Dimensionality: A Simple Example

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection

IEEE Transactions on Signal Processing
Joint-MAP Bayesian tomographic reconstruction with a gamma-mixture prior

IEEE Transactions on Image Processing
A finite mixtures algorithm for finding proportions in SAR images

IEEE Transactions on Image Processing

Object clustering and recognition using multi-finite mixtures for semantic classes and hierarchy modeling

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Model-based approaches and in particular finite mixture models are widely used for data clustering which is a crucial step in several applications of practical importance. Indeed, many pattern recognition, computer vision and image processing applications can be approached as feature space clustering problems. For complex high-dimensional data, however, the use of these approaches presents several challenges such as the presence of many irrelevant features which may affect the speed and also compromise the accuracy of the used learning algorithm. Another problem is the presence of outliers which potentially influence the resulting model's parameters. For this purpose, we propose and discuss an algorithm that partitions a given data set without a priori information about the number of clusters, the saliency of the features or the number of outliers. We illustrate the performance of our approach using different applications involving synthetic data, real data and objects shape clustering.