Self-Organizing Maps
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
Representation of functional data in neural networks
Neurocomputing
High-throughput multi-dimensional scaling (HiT-MDS) for cDNA-array expression data
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Discrimination of Coronary Microcirculatory Dysfunction Based on Generalized Relevance LVQ
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Similarity-Based Clustering
Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Learning vector quantization with adaptive prototype addition and removal
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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A correlation-based similarity measure is derived for generalized relevance learning vector quantization (GRLVQ). The resulting GRLVQ-C classifier makes Pearson correlation available in a classification cost framework where data prototypes and global attribute weighting terms are adapted into directions of minimum cost function values. In contrast to the Euclidean metric, the Pearson correlation measure makes input vector processing invariant to shifting and scaling transforms, which is a valuable feature for dealing with functional data and with intensity observations like gene expression patterns. Two types of data measures are derived from Pearson correlation in order to make its benefits for data processing available in compact prototype classification models. Fast convergence and high accuracies are demonstrated for cDNA-array gene expression data. Furthermore, the automatic attribute weighting of GRLVQ-C is successfully used to rate the functional relevance of analyzed genes.