Clinical data analysis is of fundamental
importance, as classifications and detailed characterizations of diseases help
physicians decide suitable management for patients, individually. In our study,
we adopt diffusion maps to embed the data into corresponding lower dimensional
representation, which integrate the information of potentially nonlinear progressions
of the diseases. To deal with nonuniformaity of the data, we also consider an
alternative distance measure based on the estimated local density. Performance
of this modification is assessed using artificially generated data. Another
clinical dataset that comprises metabolite concentrations measured with
magnetic resonance spectroscopy was also classified. The algorithm shows improved
results compared with conventional Euclidean distance measure.
Cite this paper
Chen, K. , Hung, C. , Soong, B. , Wu, H. , Wu, Y. and Wang, P. (2014) Data Classification with Modified Density Weighted Distance Measure for Diffusion Maps. Journal of Biosciences and Medicines
, 12-18. doi: 10.4236/jbm.2014.24003
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