A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Formulating distance functions via the kernel trick
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
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The k Nearest Neighbor classifier has been applied to the identification of cancer samples using the gene expression profiles with encouraging results. However, the performance of k -NN depends strongly on the distance considered to evaluate the sample proximities. Besides, the choice of a good dissimilarity is a difficult task and depends on the problem at hand. In this paper, we learn a linear combination of dissimilarities using a regularized version of the kernel alignment algorithm. The error function can be optimized using a semi-definite programming approach and incorporates a term that penalizes the complexity of the family of distances avoiding overfitting. The method proposed has been applied to the challenging problem of cancer identification using the gene expression profiles. Kernel alignment k -NN outperforms other metric learning strategies and improves the classical k -NN based on a single dissimilarity.