Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix computations (3rd ed.)
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Unifying Approach to Hard and Probabilistic Clustering
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multimodal oriented discriminant analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Nonlinear adaptive distance metric learning for clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Transferred Dimensionality Reduction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Web service clustering using multidimensional angles as proximity measures
ACM Transactions on Internet Technology (TOIT)
Subspace maximum margin clustering
Proceedings of the 18th ACM conference on Information and knowledge management
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Binary matrix factorization for analyzing gene expression data
Data Mining and Knowledge Discovery
Self-organizing feature map for cluster analysis in multi-disease diagnosis
Expert Systems with Applications: An International Journal
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Image analysis with nonlinear adaptive dimension reduction
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Statistics and Computing
Discriminative clustering for market segmentation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixing matrix estimation using discriminative clustering for blind source separation
Digital Signal Processing
Regularized soft K-means for discriminant analysis
Neurocomputing
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Expert Systems with Applications: An International Journal
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Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of programming and because it accomplishes a good trade-off between achieved performance and computational complexity. However, k-means is prone to local minima problems, and it does not scale too well with high dimensional data sets. A common approach to dealing with high dimensional data is to cluster in the space spanned by the principal components (PC). In this paper, we show the benefits of clustering in a low dimensional discriminative space rather than in the PC space (generative). In particular, we propose a new clustering algorithm called Discriminative Cluster Analysis (DCA). DCA jointly performs dimensionality reduction and clustering. Several toy and real examples show the benefits of DCA versus traditional PCA+k-means clustering. Additionally, a new matrix formulation is proposed and connections with related techniques such as spectral graph methods and linear discriminant analysis are provided.