Dimensionality reduction by self organizing maps that preserve distances in output space
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Semi-supervised classification by local coordination
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
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We introduce the new nonlinear dimension reduction method: LTSA, in dealing with the difficulty of analyzing high-dimensional, nonlinear microarray data. Firstly, we analyze the applicability of the method and we propose the reconstruction error of LTSA. The method is tested on Iris data set and acute leukemias microarray data. The results show good visualization performance. And LTSA outperforms PCA on determining the reduced dimension. There is only subtle change in the clustering correctness after dimension reduction by LTSA. It is evident that application of nonlinear dimension reduction techniques could have a promising perspective in microarray data analysis.