Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Elements of information theory
Elements of information theory
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
Combining Image Compression and Classification Using Vector Quantization
IEEE Transactions on Pattern Analysis and Machine Intelligence
DCC '99 Proceedings of the Conference on Data Compression
DCC '02 Proceedings of the Data Compression Conference
On the role of mismatch in rate distortion theory
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
Extracting gene regulation information for cancer classification
Pattern Recognition
Active Grading Ensembles for Learning Visual Quality Control from Multiple Humans
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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In many applications of supervised learning, automatic feature clustering is often desirable for a better understanding of the interaction among the various features as well as the interplay between the features and the class labels. In addition, for high dimensional data sets, feature clustering has the potential for improvement in classification accuracy and reduction incomputational complexity. In this paper, a method is developed for simultaneous classification and feature clustering by extending discriminant vector quantization (DVQ), a prototype classification method derived from the principle of minimum description length using source coding techniques. The method incorporates feature clustering with classification performedby fusing features in the same clusters. To illustrate its effectiveness, the method has been applied to microarray gene expression data for human lymphoma classification. It is demonstrated that incorporating feature clustering improves classification accuracy, and the clusters generated match well with biological meaningful gene expression signature groups.