SIAM Review
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Adaptive Kernel Metric Nearest Neighbor Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Application of the GA/KNN method to SELDI proteomics data
Bioinformatics
An algorithm for baseline correction of MALDI mass spectra
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Parametric distance metric learning with label information
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hi-index | 0.00 |
Mass spectrometry has become a widely used measurement in proteomics research. High dimensionality of features and small dataset are two major limitations hindering the accuracy of classification in mass spectrum data analysis; consequently, to obtain good results, the issues of feature extraction and feature selection are especially important. The quality of the feature set determines the reliability of the prediction of disease status. A well-known approach is to detect peak values and then apply support vector machine recursive feature elimination (SVMRFE) to choose feature sets for classification. In this paper, we apply a distance metric learning to classify proteomics mass spectrometry data. Experimental results show that distance metric learning can successfully be applied to the classification of proteomics data and the results are comparable to or better than, the best results by applying SVM to the feature sets chosen with the use of SVMRFE. We also perform feature reduction using manifold learning and experimental results indicate its promising potential in this application.